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Top 10 Benefits of Using "Digital Twin" Builder Establish Communications Predict Product Design/Performance

9/1/2018

“Digital Twin” is one of the top strategic enterprise trends today. Digital Twin Builder system is designed to autonomously build digital twins directly from streaming data in the edge mission space. The system is built for the emerging AI network world in which real-world devices are not just interconnected, but also offer digital representations of themselves, which can be automatically created from, and continually updated by, data from their real-world counterparts.

Builder addresses these challenges by enabling any organisation with lots of data to create digital twins that learn from the real world continuously, and to do so easily, affordably, and automatically.

Digital twins are digital representations of a real-world object, entity, or system, and are created either purely in data or as 3D representations of their physical counterparts. For example, every component of large machines can be stored as a digital twin in the Builder system.

This allows engineers only know where everything is and what it looks like, but also how well components are performing and when they need upgrade, repair, or replacement. But for most organisations that kind of massive, programme isn't an option. They need something simpler, easier to deploy, and cheaper.

Digital Twin Builder system is designed to enable digital twins to assess, learn, and predict their future states from their own real-world data. In this way, systems can use their own behaviour to train accurate behaviour models. The important difference to other AI solutions is this ability is offered as a service in real-time, without centralised, batch-oriented big-data analysis.

Key challenges include how enterprises can implement the technology, given their investments in legacy assets. Limited skill sets in streaming analytics, coupled with an often poor understanding of the assets that generate data within complex AI network systems, make deploying digital twins too complex for some. Meanwhile, the prohibitive cost of some digital twin infrastructures puts other organisations off.

Digital Twins need to be created based on detailed understanding of how the assets they represent perform, and they need to be paired with their real-world pairs to be useful to stakeholders on the front line. "Who will operate and manage digital twins? Where will the supporting infrastructure run? How can digital twins be joined up with AI networks and other applications, and how can the technology be made useful for agile business decisions?"

The ability to add AI at the edge is an increasingly important element for networks as companies look to improve data processing and efficiency. Digital twins are digital representations of a real-world object, entity, or system, and can be enhanced with AI networks. Improvements can be made to digital twin technology to help AI networks.

Robots are replacing somemotor-drive-gear-operated systems. The robots perform the same processes, for example, cutting, bending, and sealing tasks for a shipping box, but their arms are driven by independently controlled servomotors. “The old machines are much cheaper to build, but they can only do one thing. So, if you had a new package, you either had to re-engineer your machine or scrap it. With robots, it’s simply a matter of changing the packaging profiles and the application parameters.”

Still, reprogramming robots takes time. In the past, an engineer might have used applications to simulate how the line would handle the new packaging, but then have to pull those robots off the line. Then the engineer would test and tweak the movement of materials through the machine and the ability of the robotic arms to make and package containers, all the while trying to shave time off each cycle.

A digital twin goes one step beyond conventional models. It models the robotic line with such high fidelity, the engineer can do all this in the virtual world. After adjusting the model profiles and operating parameters, the engineer simply exports the profiles and parameters to the control system of the physical equipment. So it should run perfectly the first time. “That alone will certainly disrupt the packaging machine industry,” “That’s the future, and it’s certainly where digital twins for large machines will end up.”

Yet this is clearly not all digital twin technology. is capable of. In fact, simulating individual machines is only the beginning. Because the real power of a digital twin is not that it optimises a single machine, but that it interacts with the digital twins of every piece of equipment in a factory and the digital twin of every product those machines make.

And it its not limited to optimising those production processes in the virtual world. Digital twins run in tandem with their highly instrumented physical twins, fed by data by from actual operations. By comparing the output of the digital and physical systems, engineers can quickly spot problems before they arise, avoid bottlenecks, and find new ways to boost throughput and reduce costs.

In short, digital twins are the foundation of tomorrow’s smarter workplace.

Digital twins may seem like the newest buzzword but the concept dates back two decades. Their premise was simple: A digitally modeled system is really composed of two systems, a physical system and a virtual system that contains all the information about the physical system. They can exist for products and for processes.

Engineers have used product models for decades, but only recently have they achieved the extraordinary fidelity needed for digital twins. “In industries like automotive, we can define a big chunk of our products geometrically such that it is almost impossible to determine whether a representation is virtual or physical. Product models slash development time by letting engineers build and test virtual prototypes to optimise design and cost. Now the same approach is used in manufacturing processes..

Conceptually, it is not much of a jump from testing product designs to simulating manufacturing processes. In fact, many application programs do something similar today. What makes digital twins different is their fidelity and their ability to handle large amounts of data in real time.

Even modest factories are complex, and they have far fewer constraints than any complex product designed to operate in a specific way. In a factory, operating procedures are always changing. Even a simple drill press might bore aluminum one day, then switch bits, speed, and coolant for steel the next. A modern factory might make many products, and the flow of materials from machines through assembly stations will change with them.

The digital twin of a factory must be robust enough to capture those changes, plus all relevant data from each operation. That takes massive AI horsepower. Fortunately, networks have grown more powerful and manufacturers can now tap the cloud to store and analyze factory data using cognitive computing programmes.

Modeling tools have also advanced, especially their ability to generate “lightweight” models. “We can select the geometry, characteristics, and attributes we require without carrying around unnecessary details,. This dramatically reduces the size of the models and allows for faster processing.”

Reducing data requirements lets digital twins visualise and simulate complex systems without drowning in a flood of extraneous real-time data. It takes highly instrumented equipment to supply that data. While manufacturers have been adding sensors to the shop floor for decades, digital networks are making it cheaper and easier to collect up-to-the-minute factory data for performance analysis.

New design and production sites are based on digital twins and the AI/Cloud data needed to feed those models. “Plants are always changing, people are moving around, machines break and lines slow down. The digital twin will only work if it reflects the reality of the shop floor.”

Smart job sites need both types of digital twins—product and process—to work.

Digital product models contain each component that goes into a product, from screws and welds to plastic shapes and machined metals. The digital twins that drive a factory have an associated bill of process for each of those components. This “instruction manual” describes the steps needed to produce and assemble those components into the final product. Product and process twins work together.

“The digital twin can provide the manufacturing execution system with step-by-step instructions for making that product. Builder system can reference that instruction manual and perform all the coordination tasks to guide the product through the factory, setting up machines on the fly, and check that each step is done correctly.”

The twins let engineers test-drive new processes. They could, for example, add a new machine to their virtual line and see how it affects output of specific products, or test whether relocating equipment or readjusting workflow between machines improves output. The result is not just an optimised machine, but an optimised process. It will be possible to do laser-scanning on an entire factory to model its infrastructure, then drop digital twins of machinery and logistics systems into it.

Builder does thousands of virtual production runs to see if product is designed with manufacturing in mind. Simulation can be run where workers put on gloves and see if they can still assemble the product, or create assembly cells that combine collaborative robots and people and see if that helps.

Once the physical line is up and running, its sensors will send operating and inspection data to the factory digital twins. These models provide a detailed view of factory operations. By looking for unexpected variances between actual and simulated data, engineers can probe for potential problems that might reduce operating rates or quality.

Digital twins support greater automation. As orders come in, the system will make sure the proper parts are in inventory, schedule machine time, and route components from workstation to workstation with only minimal human intervention. Each step of the way, the plant autonomously checks product and machine specs against their digital twins to ensure each operation is carried out correctly and that no equipment is drifting out of tolerance.

“Digital Twin” fully connected factory linked with internet sensors and cloud analytics is still a work in progress. Yet this has not stopped engineers and manufacturers from simulating some operations with existing tools. One team created a virtual model of the production line that would build its new vehicle while still designing the car digitally.

This interplay between product and process digital twins ensured the factory could produce and assemble the parts its designers had envisioned. It also helped work out problems before production. When manufacturers offer highly customised products, the virtual factory had to be flexible enough to create the parts needed for each combination without slowing down.

To be able to introduce the new models to the market as quickly as possible, engineers laid out the new lines while the vehicle was still on the drawing board. The Design engineers rapidly went through different modification scenarios of the new models over and over again.

Accordingly, the production facilities needed continuous adjustments . Fortunately, application tools are rapidly rising to the challenge of concurrently building and integrating digital twins. Builder analyzes how vehicle design changes affected production, so it shows where to focus their attention. As the technology evolves, those tools will grow more powerful and be able to handle more complexity.

AI enabled digital twins will become more tightly integrated into plant production processes, and far more capable. They will also become smarter, using machine learning programs, a type of artificial intelligence, to learn more about factory machines and improve the ability of digital twins to simulate and predict their behaviour.

“At the outset, there is a good idea of what operating parameters should be, but it is key to improve prediction capabilities by incorporating data as the machine is operating, and learn from that data.

As AI systems learn more about specific machines, they will use their digital twins to help engineers run plants more efficiently. A suspect sound coming from a machine? AI can analyze it to see if a screw is loose or a bearing is starting to fail. The better the AI knows the machine, the more accurately it can predict when that failure is likely to happen. And the more options—fix it now, run the machine to maintenance, or readjust production schedules and take the machine offline—it can offer a plant administrator.

Digital twins are evolving rapidly. Where will it end up? More economical manufacturing of small lots, or even lots of one? Maybe. Hyper-customised products? Perhaps. Fully programmed and optimised production lines that need only a few hours of shakedown before startup? That would be great. Machines managing and controlling other machines? Closer than we think. That is what a demo is used for to automate machinery with extensive digital product and process twins to keep everything on track. The results are stunning. By using digital instruction manuals and robots to move parts from one workstation to the next, it can produce products very quickly.

Digital twin simulations can be compared to physical machine and product data, so the factory can tune and retune its equipment. This achieves remarkable levels of quality: despite churning out huge lots of different products every day. This is very much what mass customisation looks like. Eventually plants may churn out customised products nearly as inexpensively as factories that make mass-produced models.

Digital twins will make that possible, as well as a whole lot more. Their future is still being written.

But difficult as it may be to accept, sometimes “Digital Twin” terminology can get in the way of innovation. A case in point is the “model-based” cluster of engineering design, manufacturing, and enterprise terminologies and methodologies.

While “Digital Twin” implementation can be beneficial, redundancies and overlaps foster long-running confusion in both traditional worker and digital context.

In recent years, many similar difficulties have been overcome with “Digital Twin” lifecycle management strategies reshaping how many enterprises handle their data, i.e., integrated from initial concept of a product to the end of the product life and often beyond that with nothing relevant left out. As digitalisation continues its rapid penetration of enterprises, the overall economy, and DoD transformation is all around us.

This “Digital Twin” transformation and its many innovations depend on visibility, connectivity, and traceability of data whether structured or unstructured. Significant parts of this transformation are being compromised by implementations with closed-system architectures and limited connectivity.

Overlaps and the confusion they foster tend to isolate capabilities from innovative processes and workflows that are required by the enterprise. The problem is routinely encountered in the aerospace and defense industries.

When one steps back a bit, it is as if “Digital Twin” terminologies and methodologies are fighting each other for dominance, market space, and behaviour. Given their history and how they are managed and mismanaged, this shouldn’t be a surprise. That this situation persists, however, is a surprise.

In short, it's time to clean up “Digital Twin” terminology and to take fuller advantage of is required end-to-end enablement-- challenging developers, marketers, and standards committees to sort out terminology and agree on common-sense definitions with minimal overlaps.

DoD must begin with recognising that there is a problem and that it can be fixed —that logical, everyday definitions of can be agreed on by all concerned. Until this is achieved, fundamental difficulties in enabling good practices will not be solved.

When viable “Digital Twin” terminology agreements are reached, benefits can be expected quickly: The all-important exchange of information between the factory floor and design engineers will be simplified and sped up.

Better connectivity will make workflows more visible across the enterprise, so they can be leveraged at many points in the lifecycle. With connectivity and visibility comes greater transparency of processes and potential for capabilities to become widely available across the enterprise and in the extended enterprise of partners, suppliers, and customers.

“Digital Twin” solution providers will put developers to work on better-enabled framework. To grasp these potential benefits, it helps to look into the generally accepted terminologies:

Model-Based Definition refers to 3D construction application models providing specifications for a component or assembly without additional 2D engineering drawings. These specifications include geometric dimensioning and tolerancing, bills of materials, technical data packages, engineering configurations, design intent, etc.

Collectively known as product manufacturing information, these data sets and their linked repositories contain the information to manufacture and inspect the product. Model-Based Definitions are also known as “digital product definitions.”

“Digital Twin” Model-Based Design is a mathematical and visual method of addressing problems in designing complex control, signal processing and communication systems. In Model-Based Design, models are developed in simulation tools for rapid prototyping, testing, verification, predictive signals, and record libraries.

Model-Based Design is also a communication framework to represent shape, behavioural, and contextual information throughout the design process and development cycle.

Model-Based Engineering is the use of models as the authoritative definition of a product or system's baseline technical details. Intended to be shared by everyone involved in a project, these models are integrated across full lifecycles and span all technical disciplines.

Model-Based Systems Engineering is a methodology to support system requirements, verification, and validation activities from conceptual design, throughout development and on into later lifecycle phases. Like other “Digital Twin” components, engineers use these simulations to exchange information.

Model-Based Enterprise refers to an organisational work space that leverages the model as a dynamic artifact in product development and decision-making. The Model-Based Enterprise focuses on the management of lifecycle feedback to create follow-on products and their iterations and variants.

Integration considerations always bring us back to DoD specification standards and, as we have seen, they are a big part of the problem. There are at many separate groups developing standards relevant to “Digital Twins“, which impact it or join it to related processes and information-constructs.

None of these standards is adhered to by all developers and solution providers. Practice workshop attendees noted that each solution provider defines “Digital Twin” in ways that best align with its marketplace stance and competitive advantage, i.e., they define “Digial Twin” to be what their solution can do. So it is useful to use cross-discipline standards that adhere to end-to-end, “system of systems” approaches.

There is another dimension to the “Digital Twin” problem: the difficulty of implementation .

It is time to fold “Digital Twin” methodologies and associated terminologies together to fully integrate them into enterprise information infrastructures. Only then can tech be enabled with the lifecycle data and process management it requires. When this is done, one of the goals of digitalisation will be realised.

Product Innovation Platforms enable “Digital Twin” to optimise everything from customer requirements, product behaviour and performance metrics using sensors through end of service life . Product Innovation Platforms let users collaborate and innovate more effectively with seamless and transparent data sharing throughout the entire lifecycle.

Our Product Innovation Platform model illustrates the enterprise product lifecycle functional domains, with Multidisciplinary Lifecycle Optimisation capabilities overlaying the center—the Blockchain Backbone—connecting and enabling collaboration among the functional domains, as well as enabling true lifecycle systems of systems product and process optimisation.

Modern DoD demands for data sharing require that integration be reliable, which is a core value fo “Digital Twin“. Must have capability to manage new-product data across billion-dollar operations—gathering, preserving, and updates cannot be used across the enterprise without “Digital Twin.”

For the good of innovative product development and the beefing up of enterprise competitiveness, we believe it is time for everyone responsible for “Digital Twin” to take an unbiased look at the gains to be won from implementation key part of digitalisation and the sweeping transformations accompanying digitalisation, all of which are inevitable.

Unless and until agreement is reached on the need for change in “Digital Twin” methodologies and associated terminology, they will continue to be a stumbling block. Knowledgeable experts from industries using “Digital Twin” must cross traditional engineering boundaries and integrate team work orders. Benefits of using Digital Twins include:

1. Helps you move validation processes into the virtual world – but still keep you connected to how your products act in the physical world. This virtual-physical connection lets you determine how a product performs under a number of conditions and make necessary adjustments in the virtual world to ensure the physical product will perform exactly as planned in the field, reducing risk. Digital twins help you navigate world of complex systems and materials to make best possible decisions with confidence.

2. Helps you validate how your production process will act on the shop floor before anything actually goes into production. By perfecting this performance using your digital twins, and understanding why things are happening using the digital thread, your prevent costly downtime to machines and robots on the shop floor. You can even predict when maintenance will be necessary to avoid unnecessary downtime.

3. Helps you save time and money in simulation, testing and assessments so you no longer have to rely on only physical constructs; instead, you can include information from physical performance in your digital twins to maintain a high version of fidelity and reality in the virtual world. This constant stream of accurate, updated information gives you the situational awareness you need to make decisions faster, increase your production speed and optimise your productivity to get to market faster.

4. Helps you develop intelligence to feed advancements and reduce risks in the future products. Machine data collected over a period of time can enable digital prototypes to sustain life of product and help human operators make better decisions to enhance product performance

5. Helps you generate production data in real time to reflect the current and future performance status of their physical counterparts to enable sharing with technicians or other interested groups remotely. Virtual representations will be able to predict faults and errors and help avoid costly implications.

6. Helps you generate value in the form of less maintenance costs, new revenue streams, and better management of assets. As technology improves and virtualisation options become more pronounced, you will be able to deploy digital twins with even less capital investment while deriving greater returns on investments in a shorter time period.

7. Helps you be able to provide an integrated outlook of any project, to any user, at any point of the product lifetime. This single source of validated information allows you to foster collaboration across various teams and departments, and even outside the organisation. Engineers can simulate the behaviour of complex systems to predict and prevent mechanical breakdowns.

8. Helps you advance existing processes, products and services and often lead to new market opportunities while significantly cutting operating costs, leading to real bottom-line improvements. Digital twins help you propel traditional manufacturing to a new competitive level via intelligent connected products.

9.Helps you improve product performance while mitigating both the cost and risk of a new product introduction. Digital twins can dramatically speed product realisation time as you decrease or eliminate the most time-consuming aspects of building products in the real world. Early discovery of system performance deficiencies uncovered by simulating results before physical processes and product are developed compresses time to value relationship

10. Helps you demonstrate product value proposition before build stage with opportunity to link organisational tools, skills and knowledge bases. It’s becoming increasingly apparent that different manufacturing concepts and methodologies are required to take a product idea to commercialisation. Continuous refinement of design models is possible through data captured and easily crossed referenced to design details.
　

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Top 10 Benefits to Prototype Process for Design Exploration Improve Ability to Meet Product Goals

8/9/2018

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Digital prototyping can be used by design teams at any time in the design cycle, from conceptual design to production. It’s a means of understanding performance and a step toward design optimisation. Digital prototyping brings form and function together to solve a problem or drive innovation. The entire development team may use a simulation that represents product function under operating conditions. It’s a logical alternative to developing each aspect of product design and engineering separately.

Physical prototypes let development teams test for fit, actuation, operation, instrumentation, and controls. That’s been good enough for a long time. But conventional physical prototyping also can turn the development process into an inefficient, linear affair.

A concept is sketched, the product engineered, and the physical prototype built and evaluated for issues and adjustments that are required. With each prototype failure, the process starts over again. But the electrical engineer, for example, may not have the opportunity to advise the industrial designer “out of sequence” and share insight that could help the designers avoid spending time on a pointless undertaking.

The goal of digital prototyping is to bring all geometry and functional characteristics into a single model. So digital prototyping is an approach that favors a two-way pipeline over the conventional, linear sequence. Information flows to and from that model during the industrial design, engineering and manufacturing teamwork.

Digital Prototyping gives you the ability to explore a complete product before it is built so they can carry out product design, visualise, and simulate from the conceptual design phase through the manufacturing process.

By using a digital prototype, manufacturers can boost design performance and innovation by visualising and simulating the real-world completion of a design and save time and money by reducing the number of physical prototypes they build.

Digital Prototyping brings together design information from all phases of the product development process and facilitates manufacturing work groups to create a unique digital model that can be used in every stage of production bridging the gaps that usually exist between conceptual design, engineering, and manufacturing organisations.

The solution for Digital Prototyping is different in that it is scalable, attainable, and cost-effective. This solution enables a wider group of manufacturers to realise the benefits of Digital Prototyping, with minimal disruption to existing work flows providing the most straightforward path to creating and maintaining a single digital model.

“Prototype” is a test article designed to demonstrate areas of high technical risk that are essential to system success. A prototype need not be a full system, but, in scope and scale, it is tailored to accommodate series of decisions so it can represent a concept, subsystem, or end item according to the decisions to be made. Rather than reflect the final design, prototypes are built with the expectation that, as decisions are made, change will follow.

“Prototyping” is the practice of testing prototypes, of appropriate scope and scale, for the purpose of obtaining knowledge about some requirement, capability, or design approach. The knowledge obtained informs a decision-making process with output to result in some degree of change. The degree of allowable change is bounded in proportion by the scope and scale of the prototype.

A prototype is a working model of a product that is used for testing before it is manufactured. Prototypes help designers learn about the manufacturing process of a product, how people will use the product, and how the product could fail or break. A prototype is not the same thing as a model. A model is used to demonstrate or explain how a product will look or function. A prototype is used to test different working aspects of a product before the design is finalised.

Your team will follow a similar process. By building a prototype, you should be able to determine if your chosen design solution is feasible and which aspects of your design needs special materials or further refinement. You will also ask other people to test your prototype to help you identify any problems a user might encounter. You may even have time to complete a few iterations, or modifications, to your prototype.

No matter where you are in the process and no matter how much experience you have, working with a dedicated prototyping shop is valuable. A prototype gives you a tangible object that you can hold in your hands, inspect and test in the real world. It’s also a chance to see exactly how a part comes together during assembly and catch errors or omissions before final production.

We understand the value of a prototype and have a dedicated prototype shop to help meet customer requirements and suggest materials and part geometry based on solid experience.

Streamline your entire product development process with our solution for Digital Prototyping, which lets you explore your ideas before they’re even built. Gather design information from all phases of the process into a single digital model. Validate it against product requirements. Reference it as you build deliverables for release.

A prototype is anything that represents your product work, in a tangible format, to actual customers for the purpose of gathering feedback. It is a testable proposition about value or technical feasibility. A good prototype minimises the time and cost needed to get smart about a new offering.

Early on, when you’ve identified some key ideas and need to understand which ones are worth pursuing, storyboards or paper mock-ups are appropriate. As you continue, you’ll want to know how a selected idea is best translated into a design. At this point high-fidelity mock-ups or a non-production implementation of a key interaction are ideal.

Each new project is assigned to team charged with oversight of all stages: quoting, design, programming and fabrication. Team attention to your requirements reduces miscommunication along the way and keeps your project moving.

Many original equipment manufacturers use hard tooling to manufacture large quantities of parts in their own factories. When it’s time to update a part or create a new one, they can’t always afford the time and money it takes to manufacture a new tool set on-site just to test new parts.

Rapid prototyping shops take the prototype designs and making tools for them. Recently, our team manufactured and tested a new punch and die set for a client. As a result, the company quickly learned that it was worth the investment in this hard tool without having to divert time and resources from its regular manufacturing.

What if you’re developing a new product or have proprietary parts to test? Is your design classified? Can you still get a prototype when the fabricator truly can’t know all of the details?

There are many times we do not know the end use of a part or assembly that we’re making for a customer so we will ask for as much information as they can give and offer suggestions for design improvements as we can see them.

We have worked with enough types of material and parts to identify clues and make improvements even without the full picture. Having years of experience means knowing the optimal thickness for hot-rolled steel sheets or if laser welding will increase joint strength even if the final application is a mystery.

Working closely with clients is key. In a recent case, we worked to improve a design they were bringing to market. After many meetings and prototype sessions with their engineers, and sharing their sheet metal fabrication knowledge, the final product was easier to manufacture.

As we’ve said before, there’s more than one way to design and build a part. By following manufacturing design principles, prototype fabricators can facilitate assembly, or speed time to market. We do deep dives into designs looking for ways to refine and improve the final product.

In one recent case, we took a three-piece welded design and changed it to a one-piece design that was cheaper and easier to manufacture.

In-shop fabrication equipment plays a role too. Offering laser welding, we are able to take an enclosure that was originally welded and finished with grinding and changed it to a laser welded design. The result? A part that works better and is cheaper to manufacture.

Laser welding gives you an opportunity to redesign parts for more efficient production. Some of the greatest advantages come from simpler joints, reduced overlaps and welding in places that might otherwise be inaccessible.

If you have a design ready to fabricate or if you’re still working out the kinks in your plan, request a quote or contact the team to find out how our express prototype shop can help.

In a perfect world, there would be no deadlines.

Ideally, you would get the chance to exhaustively explore all possibilities for a design. You could develop many different alternatives, ensuring that you arrive at the best choice for all design characteristics.

Of course, it’s not a perfect world. And, like everyone, engineers can find themselves facing a looming deadline. In these frequently encountered cases, they can end up desperately exploring a couple different choices before going with the first feasible design.

There are big challenges facing engineers that keep them from exploring the design space for the items they’re developing. Emerging technologies can help mitigate those barriers.
　
One reason deadlines today have become so challenging for engineers is that the expertise of one person can only cover so much. As products become more complex, so do the manufacturing and technology options that can be incorporated into the product, and the number of engineers with particular specialties—mechanical, electrical, etc. —working on the product rise.

You’d like to carry out your job and pass the design to the next engineer for approval and enhancement. But of course, that cycle needs to end at some point.

Also, you, like many other engineers, may have a specialty within your discipline--you may work primarily in plastics or sheet metal, machined products or composites, for example. To exhaust all design possibilities that could result in a high-performing product requires knowledge of every aspect of your field and every other field involved in that product’s production, which isn’t usually a practical expectation.

You also need to weigh how fully you need to explore your designs. Is meeting the minimum on a project good enough? Or would further exploration, iteration, and improvement result in a justifiable return on your project?

Even if you answered that meeting the project minimum is good enough, nothing may make you happier than the freedom to fully explore your design space, even as that the project deadline nears.

As an engineer you are a problem solver. Left alone with a design you’ll likely want to explore, design, iterate, and repeat the cycle until you come up with a great design solution for the situation at hand.

And sometimes a design really needs to be the best one, rather than the first feasible one, In that case, further exploration does result in a justifiable return on your time.

But too often, exploration can feel cut short because you work within competing constraints of the design cycle. On one hand, the design needs to be released on time. On the other hand, you need to spend time developing a design that meets all specification and requirements.

Your freedom to explore their design space to varying degrees is based on these competing design-cycle demands. The demands themselves are at two extremes; between them is the huge range you have to explore.

There are many legitimate scenarios in which you do need to go with the first feasible design for a number of reasons. Likely, a deadline looms and design needs to be passed off to the manufacturing floor or sent out to a supplier, even as you’d like to explore a few other design possibilities, check out a few other results even after you’ve found a feasible design.

Today, shorter and shorter development schedules can force you, even as you pass back and forth, to make compromises that come in a few different forms. Maybe you feel the model geometry could be further perfected or maybe you’d like to run a few more assessments for virtual prototyping reasons. But time runs out.

In all, you may not feel so great about your lot in the development cycle. Sometimes, you might feel like they aren’t given the room you need to truly develop real design solutions because you’re forced to go with the first feasible design.

It is in this context that the objective and associated enabler is revealed. For you to find something better than the first feasible design, your organisations must accelerate design exploration.

To do that, you must be enabled to be more productive in design. Today, there is a range of emerging design processes and technologies that can do just that.

How fully are you able to explore your designs? How and when is it justified to exhaustively explore design alternatives and why is that important?

Rapid prototyping technology is gaining a lot of importance among the engineering designers and manufacturers, as it offers a fast and accurate way to realise the potential of the product. Adopting rapid prototyping brings higher fidelity from the concept design compared to the conventional paper prototyping.

The use of rapid prototyping in product design and development is a profitable decision and must be encouraged in the manufacturing organisation. In a fiercely competitive landscape, this tool can help in developing innovative products cost-effectively.

Rapid Prototyping utilises a group of technologies used to quickly fabricate a scale model of a physical part or assembly using 3D printing/design. Prototyping isn’t a new concept, but techniques have been refined to advance many industries, including fabricated reflective optics.

A common misconception about Rapid Prototyping is that a machine is simply loaded and a finished part magically appears. In fact, the majority of work involved in creating a prototype happens well before a prototype is created. We first work with customers to determine the specific prototype needs; then our engineers pinpoint which of the Rapid Engineering technologies will produce optimal results.

3D Printing and Modeling are the most common methods, using equipment that produces 3D components in various materials including plastics, wax, and metal. While effective, this technology can’t directly produce a fabricated reflector. Instead, we use 3D Printing and Modeling for rapid tooling and fixture needs. It dramatically reduces prep time, which otherwise takes weeks using traditional methods.

Laser and Punching are the most traditional Rapid Prototyping methods for fabricated reflectors. These technologies continue to mature and advance, making them the core of versatile and rapid fabricated metals processing. Laser and Punching are programmable and changeable so the process can continue until the part is right.

Designers, more often than not, ignore the process of prototyping before finalising concept models. Most think prototyping is a waste of time and money. Couldn’t a 3D model achieve the same result? Some are simply confused about what prototyping actually is.

Prototyping across many industries refers to creating a highly realistic model of the product solely for testing or promotional purposes. It’s different to building a computer or 3D versions of design models, which are hard to interact within a realistic sense.

Prototyping is both progressive/practical and can be one of the many stages of developing a high-quality, error-free product.

Using the following techniques provides you with significant advantages:

1. Prototyping with Reduced Costs in Development is Possible and Available

Much of the costs of prototyping in the past were associated with expensive materials used in the products. Nowadays, prototypes can be made for far less using inexpensive and alternative materials. Cheaper new materials can realistically replicate shapes and features of products, and ultimately mimic real functionality as well.

We specialise in creating concept prototypes using inexpensive material without compromising the overall quality of the prototype experienced in visually replicating products near exactly as prototypes. We employ a wide range of manufacturing tech to develop prototypes, some of which allows for creating different prototypes at the same time for side-by-side comparison. If your budget isn’t big enough for a specially made concept, you could always purchase materials and build your own prototypes.

Prototypes do cost money, but the benefits they offer can eliminate many expensive costs associated with errors. Think about how costly it is to fix problems in products discovered after the product goes to the market. Designers, developers and others can use prototypes to fine-tune final products in a manner that typical concept art does not allow. Companies not only have to pay developers to correct mistakes, but there are additional costs. In an overall sense, prototyping can actually save your company money.

Prototyping is also much less costly than ordering samples of the final product. Companies often place sample orders to test products before mass-market retail. These sample orders are the real product, so each would cost more. Prototypes are far cheaper to make realistically for testing purposes. As mentioned above, being specialised and seasoned manufacturers, we can deliver the perfect testing prototype for the fraction of a cost of an original sample. All in all, companies can save significant amounts of money in the long term by using prototypes.

Prototyping injection mold tools and production runs are expensive investments. The 3D printing process allows the creation of parts and/or tools through additive manufacturing at rates much lower than traditional machining.

2. Speed Up Time-to-Market

3D printing allows ideas to develop faster than ever. Being able to 3D print a concept the same day it was designed shrinks a development process from what might have been months to a matter of days, helping companies stay one step ahead of the competition.

With additive manufacturing, time required to develop molds, patterns and special tools can be eliminated. The same CAD applications and the printing equipment can be utilised to produce different geometries. Unlike conventional prototyping methods, the amount of waste produced is minimum, as rapid prototyping only prints the material that is actually required to build the object.

Manipulating geometry in 3D space presents hard to see opportunities very early; before everyone has committed to the first reasonable idea. At the point of launch, new products often have things that could have been done better in hindsight. Even with this being so there is no reason to make sure by not using tools that are already available to use.

Anyone can make anything once, but what about the second time, and every time after that? The prototyping process always represents the production process, ensuring consistency and a quick start-up of the production process.

As the name implies, Rapid Prototyping produces results quickly. However, it’s also understood that while prototyping is rapid, it’s never rushed. Experience, engineering, and technologies combine for best outcomes.

Speed, better results, streamlined transitioning and easy refinements – mean you’re able to get your product to market faster. And the profitability that goes with it.

In the modern boom of digital art and design, the possibilities are not only accelerating but limitless. One can now 3D print almost anything they imagine after drawing it up virtually. In a relatively short time, an idea, concept, dream or invention can go from a simple thought to a produced part that you can hold.
　
3. Verify Design Concepts to Mitigate Risk

Being able to verify a design before investing in an expensive molding tool is worth its weight in 3D printed plastic, and then some. Printing a production-ready prototype builds confidence before making these large investments. It is far cheaper to 3D print a test prototype then to redesign or alter an existing mold.

Rapid prototyping allows designers to realise their concepts beyond virtual visualisation. This enables to understand the look and feel of the design, rather than simply assuming through the CAD model. This helps designers to carry forward their ideas and implement them in their design prior to finalisation. It also provides a proof of concept for the end client, seeking for a more realistic product design rather than merely visualising the design on screen.

With a prototype you can test the market by unveiling it at a trade-show, showing it to potential buyers or investors, or raising capital by pre-selling. Getting buyers response to the product before it actually goes into production is a valuable way to verify the product has market potential.
　
4. Minimisng Design Constraints/Limitations

The additive manufacturing offers the ability to identify flaws in the design prior to mass production. The materials available for rapid prototyping closely resemble the properties and strength of the actual product, making it possible to perform physical tests easily. The risks of faults and usability issues can be identified earlier to avoid problems that might occur later during manufacturing process.

There’s nothing inherently risky about integrating geometry and performance data into a single model to simulate function—unless the design team is afraid to find out that their assumptions are wrong. The value of bringing this technique into the design process sooner, rather than later, is that it helps to eliminate invalid design options and isolate those that are going to be most advantageous.

The limitations of standard machining have constrained product design for years. With the improvements in additive manufacturing, now the possibilities are endless. Geometry that has been historically difficult or impossible to build; like holes that change direction, unrealistic overhangs, or square interior cavities, is now possible and actually simple to construct.
　
5. Customising Designs

The most promising benefit of rapid prototyping is the ability to develop customised products as per the individual requirement. It requires no special tools or process to implement design changes in the product. A small change in the CAD model and the entire process remains the same. For manufacturers, this is highly advantageous as it offers a connected experience for the customer with the product they purchase.

With standard mass-production, all parts come off the assembly line or out of the mold the same. With 3D printing, one can customise fits. One thing you can’t get from a picture or virtual prototype on the computer screen is the way something feels in your hand. If you want to ensure fit of a product is just right, you must actually hold it, use it and test it.
　
6. Proof of Design Concept

Many developers overlook proof of concept prototypes because they have a short lifespan. Proof of design prototypes may not survive rigorous testing, but these models provide valuable information regarding how practical the design concept is. These prototypes can facilitate the creative process of design and point out mistakes early in a highly realistic manner.

Developers can have several concepts prototyped to make informed, digital-based decisions on which models to move forward with. Prototyping can eliminate some confusion of the design process as well, especially if multiple teams or many employees are involved. While these prototyping models are not built to last, they can be highly valuable in many other ways.

Being able to test ideas quickly and discover what doesn’t work accelerates discovery leading to an ideal solution. 3D printing allows a product developer to make breakthroughs at early stages that are relatively inexpensive leading to better products and less expensive dead-ends.

7. Clearly Communicate Results to Customers

Describing the product you are going to deliver is often misinterpreted since it leaves construction up to the imagination. A conceptual picture of the product is better than the description since it is worth 1,000 words, but getting to hold the tangible product-to-be, in hand, clears all lines of communication. There is no ambiguity when holding the exact, or at least a very close, representation of the product.

An accurate prototype is critical in evaluating and verifying function, fit, and finish of the product. Precision is the basis of Rapid Prototyping. Once you’ve gone through the Rapid Prototyping process, revisions and modifications to products are much easier to implement.
　
8. Receive Better Feedback

Prototypes are designed to scale. Interacting with a prototype is not so different to interacting with the real product. So designers can use prototypes to obtain valuable user feedback to improve the final version. User feedback with prototyping is complete and is not based on a disproportional version of the product. Designers can rely more on such feedback to make improvements to the final product.

One major advantage of prototyping is that companies can present audiences with a highly realistic model of the product. Therefore, the resulting feedback would be as if it were based on the real deal. Prototypes can also be used highly effectively for visual presentations for feedback from clients. Visual presentation prototypes can eliminate doubts and misunderstandings when it comes to gathering high-quality feedback or initial impression reviews.

Using prototypes is highly recommended before a product moves to mass manufacturing. Prototypes can eliminate much of the problems associated with mass production as well. Designers should use prototypes to validate digital representations, reliability,and functionality of models. The cost savings down the line are significant when you create a prototype first. As the prototyping process has become cheaper in modern times, it’s a cost even startups can now bear.

9. Incorporating Changes Instantly

Having a physical model in hand, it is possible to incorporate the changes instantly merely by asking the feedback from the customers. Prior to finalising the design, there are several iterations required. With each iterative process, the design improves further, building confidence for both the designer as well as the end consumer. This also helps in identifying the actual need of the market, making possible to develop competitive products with better acceptance rate.

Furthermore, early low-volume prototyping allows affordable design modification. Modifying final product designs requires tooling modification, which is very expensive. Prototyping, on the other hands, doesn’t require tooling, so developers can benefit from surprisingly low costs. If a product needs to be rushed to the market, prototyping can speed up the process and eliminate typical errors.

10. Rapid Implementation

Prototyping can fast-track the implementation process by removing many of the quality bottlenecks product launches face. Prototypes can help designers and developers immediately spot major problems present in products. Because prototyping occurs during the development stage, that still leaves plenty of time to do a course correction if necessary. The result is fewer or no errors during the final product unveiling stage. Bugs in products can hurt sales and brand reputation. So using prototypes in advance to determine whether users can interact with the product as the designer intends helps perfect the final product that goes into the hands of the customer.

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Top 10 "Digital Twin" Simulation Working Group Objectives to Model Product/Battlespace Regions

8/1/2018

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An awesome new Digital Twin processing feature is now available!

Digital Twin Builder is a new product that builds on previous products and consolidates them into a convenient workflow exclusively dedicated to helping engineers design digital twins.

The feature allows you to check operational variables through a user-defined region. User defined regions is an amazing tool when you want to understand flow between sections of the battlespace separate mission components, or even between different sequence of events.

Digital Twins are not only giving us the ability to do things faster, better and cheaper but it is changing the way we look at the entire product lifecycle. No longer are we tied to the iterative design cycle, but instead we are able to use simulation and other tools to inform our design choices from the very outset.

A Digital Twin, in case you missed it, is a 3D virtual representation of a component, assembly or system that is connected to its real-world counterpart by means of sensors and networked devices. The digital twin behaves just as the real-life twin behaves, and can be used for diagnosis, prognosis, and what-if scenarios of its real-world variant. The data that the digital twin provides can be used for process optimisation and maintenance scheduling, and can even be used to increase profitability by reducing downtime.

A digital twin can be as effective as the user wants it to be, and is limited only by how they choose to programme it.

A Consolidated Workflow

“Twin Builder is built on existing technology and we have invested in reduced order modeling, which is something we’ve been involved with for a few years, so Twin Builder is an extention of work that we have done in the past.

“Reduced order modeling typically takes data from 3D simulation and converts it into a faster simulation. The techniques vary—from matrix reduction, equivalent model extraction, regression, polynomial fitting, transfer function/system identification, etc. We also have techniques that can render the field back from the reduced model.

“One extra thing that we did add to Twin Builder, which has taken a couple of years, was to add connectivity to some popular network platforms. That involved phasing out some old stuff and actually making the connections with the new network platforms, so that’s the new piece.

How to Build a Digital Twin

So that’s how Twin Builder came into existence. But how did engineers build digital twins before these technologies were put together? Was there a proper and accepted method for creating them?

There wasn’t really a clearly defined method of building a digital twin in the past. What people would do in the past would be to just build a virtual prototype. There was no real notion of connecting it to operational data. And there was no real notion of actually deploying the digital twin at scale in an operational setting.

So, if you think about it, simulation is typically used at the design phase. You would build one virtual prototype for your design, validate it, and that’s it. But now what we want to do is to take that design and replicate it as you would with other virtual assets. That concept of scaling out is what’s really new here.”

“We start by telling customers to look at their top service costs that they may have; they can then do some kind of failure mode analysis to determine exactly what kind of digital twin model they need to build.

“We can then look at existing simulation models that were used during the design of the system, subsystem or component that was identified as key to the failure mode. From these simulation items, we can extract—via techniques like reduced order modeling, behavioural modeling, etc.—an accurate virtual replica of the physical equipment. Then you validate it by tuning and optimising parameters to accurately match measured data.

“Finally, we can export out the model into an executable deployable runtime. We have connectors for several popular network platforms, allowing customers to actually connect the simulation model to data through the platform and deploy the digital twin at scale,”

Use Cases in Industry

So, who are the biggest users of digital twins at the moment?

“At the moment, the digital twins are limited in deployment and limited in scale, but generally the most common uses of digital twins at the moment are for industrial equipment such as motors and pumps, and heating and cooling systems…those types of applications.”

We have heard the word “scalability” used when describing the benefits of digital twins. This can refer to the scalability of the number of instances of digital twins, or it can be used to describe the number of sensors and the scale of the analysis you want to perform.

So that begs the question: How big can a digital twin be in terms of scope and complexity?

“The scale of deployment will vary a lot. For example, in one application, we have a motor, and it has a few sensors on it. The number of sensors is very limited in that case—we are just using them to determine useful life and things like that. Then there are more complicated systems such as submersible pumps that contain multiple subsystems and multiple sensors. These are complicated systems and can contain tens to hundreds of different sensors.”

And those sensors aren’t restricted to physical sensors. Virtual sensors play an important role, too, especially when it comes to generating graphs outside of the typical functional data that is representative of nominal physical system operations. In short, a digital twin can be as big or as complicated as you want it to be. But then it really boils down to a question of what information do you need? What is useful and actionable?

Trials

“We’ve done several trials. Because this is based on existing technology, we have been testing at low scale in a few different industries with some of our advanced customers. Now we have the opportunity to embed simulation solutions for digital twins into manufacturing and asset management portfolios.

This solution will allow customers to study engineering-related effects in advance, through simulation. The solution will be run on cloud platforms, with the goal of enabling those who manage industrial facilities to optimise operation and maintenance through real-time technical insights.

"The result should also, in its extension, lead to reduced product cycle times and increased profitability.”

"The merging of physical and digital worlds disruptively affects the way in which products are manufactured, placed on the market and operated. By utilising the insights produced by digital twins, users will be well positioned to exploit the breakthrough this technology brings, and the solution will also help drive innovation.”

These solutions can not only simulate products and facilities during the product development phases, but also in manufacturing and—more importantly- even when the products or facilities are in the hands of end-users.

Based on solutions for digital twins, operators can test which flows in the most complex structure are most efficient to run under specific conditions. Through this type of simulation, it is possible to iterate and select a line of operational benefits.

These simulations use the enormous amounts of data generated by sensors in the assets, and let engineers gain valuable insights that can improve a process and provide a basis for improving future similar processes.

Additionally, one might consider developing hybrid models that leverage machine learning with multi-physical simulation models to accurately predict why a process in a facility may fail after it has been implemented.

Replacing Scheduled Maintenance with Needs-Related Maintenance

Linking these insights and data into business processes for controlling and managing plant facilities with other relevant platforms is an important step forward in the digital twin strategy.

"Combining the physical and digital worlds can sharpen competitiveness.

From a lifecycle perspective, companies can benefit from real-time insights by tracking how assets are designed, built and operated throughout the lifecycle of the product.

With the new solution, time-based maintenance of industrial assets is replaced with predictive maintenance. The machine or plant components can tell ‘themselves’ when they become worn to the point that they are likely to break.

Users can get a correct insight using a combination of real-time and predictive analyses and get Twin Builder to build, validate and distribute digital twins.

"Capturing Value Throughout the Entire Life Cycle"

Digital Twin technology simulates behaviour in different environments and stresses so the system is intended to predict problems before they occur. The prediction is based on information from physical sensors and physics-based models to provide results in 3D visualisation.

Putting together the digital and physical asset will enable capture of value throughout their product lifecycle. “This solution helps equipment operators and service providers predict and improve asset performance and reliability with technical insights. A digital twin that merges technical models, manufacturing details and operational insights, is unique in the industry.”

When we’re asked what the jobs entail, we say we’re problem hunter—solutions architect. When a company defines goals for its engineering team like doubling productivity, eliminating errors or reducing repetitive tasks, we hunt for any problem that could prevent them for reaching the goal and then design a custom solution for solving it.

It is true that many problems are similar for most engineering teams, regardless of the type of product they design or industry they serve.

For example, large numbers of engineers would mention large assembly slowdowns affecting their team. While the symptoms are the same, the causes are, most of the times, unique for each team. Finding these specific causes and tailoring solutions for each customer is art as much as science.

Here’s the lowdown on how simulation is shaping design, and how it is not only changing the products that we design, but also changing the way we look at the design process as a whole.

What Is It?

First up, let’s get up to speed on the concept of simulation-driven design.

“Simulation-driven design is taking simulation technology and moving it from the middle and the late cycles of the design process to the very front of it. This drastically lowers the time it takes to develop products, because instead of going back and forth between detailed design and validation, we put validation or simulation at the front of that process.

We use simulation to design the product using things like surface interaction optimisation, or we integrate control systems at the earliest stages, and then when we get to validation, it’s a simple check box instead of an iterative process.

So, simulation-driven design is putting simulation at the front of the design process and using simulation technology to create a design instead of using simulation to figure it out later.”

This is very much a common thread in industry manufacturing and design. In the old days, we used to have to wait for a design to come downstream before testing, building or simulating a design. Then, if the design wasn’t up to scratch, we would literally have to go back to the drawing board and try again.

Now, armed with design and simulation applications, we can effectively simulate early, and decide what strategy to use before getting too deep into the product lifecycle. We are now entering a world of pre-validated design.

How to Do It?

We’ve been hearing a lot about generative design and part interaction optimisation these last couple of years, with all of the big companies having their own take on this new way of doing things. Largely, it has been spurred on by the rise of additive manufacturing, which is permitting the creation of new geometries never before seen in manufacturing.

“This is a completely different concept that started as additive manufacturing technologies made it more possible to print these organic surfaces and structures that previously you couldn’t machine or cast because it was an organic, bone-looking design, right? When that change happened simulation-driven design really started to move from design validation, where you used simulation to validate a design, and change it from validating to becoming the actual main driver to inform the design.”

We have several products geared toward this new trend. It’s our flagship product…it’s essentially becoming a full-blown environment for simulation-driven design. It has topology optimisation and generative design capabilities to create these designs. It has finite element assessments for components of those designs. It’s got motion tools in there to understand the mechanical performance, evaluate loads, and take those loads into optimisations—that’s something that no one else can do.

We can basically do a motion simulation, and bring those loads and motions into an optimisation. We’re optimising full assembly-level designs, which is something that no one else is really doing either. We can have multiple components all being optimised at one time.”

But is it really that easy to use? It’s all very nice having generative design, which presents you with a variety of design solutions at the click of a button. But while the product lifecycle may be moving into our future, modeling itself is still a fairly entrenched task that seems destined to remain in the past. Not so true anymore.

“We created a whole suite of modeling tools that allow you to take those concepts that are generated and quickly generate smooth, organic surface design. If you’re familiar with parametric modeling, it’s very difficult to do organic surfaces in. We make it very easy. It’s call ‘3D tracing for engineers,’ so you’re basically tracing over the top of an optimised shape and you end up with a final printable design or a design that’s ready for machining.

Even if you’re unfamiliar with the Digital Twin, there’s a good chance that you use something of the sort anyway.
“A polygonal model is where you’d use blocks to model things…and what we did was we said that for every Block, you can hook those together. It’s almost like molding clay, but you’re still getting fully defined parasolid geometry that can be used in any manufacturing process.

Did you watch the video? Pretty cool, huh? The designer makes it look so easy. Now that means he’s either a simulation guru, or else it’s super easy to use? Or maybe both?

“This environment was built for design engineers—someone who is designing parts who needs to come up with lightweight and efficient structures. We’ve taken the complexity of simulation and made it very simple. You’ve heard simulation is tough, but we made it easy enough for everyone to use.

We’ve taken that to the next level. If you look at the ease of use of our interface, we’ve made it very easy to train designers how to use it—and once they start using it, they really become hooked on the style of modeling. And that has really been driving the interest in simulation-driven design.”

So now design engineering does look fun—almost like a video game.

Who Is Using It?

“The main driver that interests organisations to start with is the idea of lightweight. You hear about lightweighting all the time—these were the types of products that were initially interested in simulation-driven design. So, obviously the aerospace industry, anything going into space, any major unit that has a mass budget instead of a dollar budget—they’re using this to take as much weight out of the design as possible. The by-products of that are an accelerated development cycle as well as cutting costs by material reduction and a compressed design cycle.”

“So, ground vehicles, aerospace, general machinery devices are now starting to use it, especially as we look at structure optimisation, being able to encourage block growth through the structure—those things are very important, so we’re seeing more traction there as well.”

The Path to Mass Adoption

So, you’ve seen the features available in generative design-oriented packages. With something so revolutionary available to engineers, we should be seeing a lot more of these highly optimised designs trickling into our daily work, right?

Not quite. There is resistance to mass adoption whenever a new technology comes along, not just due to the technological hurdles but also due to behavioual hurdles.

“On the behaviour side regarding the more organic shapes…it’s half the weight yet twice as strong, which is pretty counter intuitive to engineering principles, right? There’s that old mantra of when in doubt, build it stout…simulation-driven design takes that mantra and reverses it and says that’s not the best way to do it. The best way to do it is to put the material where it needs to go so it accommodates the forces that you’re designing for. So, it creates these very organic structures.

People think that more material makes it stronger—and that’s not the reality, so that’s one of the biggest issues is getting past that.”

That’s the biggest challenge to adoption—changing the way that people think about design, from the old concept of form follows function to the modern paradigm where form follows forces.

“Also, there is resistance to changing the way people design. Any time there’s a big change in the way that we design something, there’s always resistance to change, so that’s the biggest challenge to adoption—changing the way that people think about design, from that form follows function to now, where form follows forces.”

What’s Next?

“We’re still in the early stages—traditional design groups are starting to adopt this approach—we’re seeing this adoption more in the bleeding-edge companies, right? These kinds of companies and organisations are really pushing the edges of that, and now we’re starting to see that trickle down into traditional manufacturing industries as well.”

“As we look into the future, we are going to see topology optimisation techniques hooked up to iterate through numerous different concepts very quickly—so you can look at a lot more concepts and understand what’s going to work and what’s not going to work. Then you take the best concept and move forward with that design.”

Other than having fun solving difficult problems, the declared goal has always been to facilitate—through brainstorming—the finding of new techniques and methods for maximum benefits.

Here we present goals for a Working Group organistional structure. Forum participants find answers to established questions. Most of the problems are relevant to specific groups of users who benefit from the limitations identified in the challenges and enhancement requests that are created at the same time.
　
1. Formalise planning development, integration, and use of models to inform enterprise, programme decision making, support engineering activities to digitally represent the system of interest

2. Ensure models are accurate, complete, usable across disciplines to support communication, collaboration and performance and decision making across lifecycle activities

3. Provide enduring, authoritative source of secure authentication with access/controls to establish technical baseline, product digital artifacts, and support reviews for accurate decision making

4. Incorporate technological innovation to establish end-to-end digital engineering enterprise and foster conditions for productive step advances towards goals

5. Enable end-to-end decision making using advance human-machine interactions

6. Establish mature supporting digital engineering activity infrastructure to perform activities with connected information networks

7. Develop, mature, and implement technology tools to realise digital engineering goals and share best practices using models to collaborate with stakeholders

8. Improve digital engineering knowledge base, policy, guidance, specifications, and standards and streamline contracting, procurement and business operations

9. Lead and support digital engineering transformation efforts, vision, strategy, and implementation to establish accountability to measure and demonstrate results across programmes

10. Build and prepare workforce to develop knowledge, competence, and skills with active participation and engagement in planning and implementing transformation efforts　

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Top 10 3D Print Services Support Manoeuvres Artificial Intelligence Reduce Repair Pitstops and Supply Runs

8/1/2018

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New contract to apply artificial intelligence to Marine Corps maintenance could streamline logistics and help lessen dependence of fighting forces from long supply lines. Ultimately, AI could enable the far-ranging manoeuvres envisioned by the multi-domain operations concept.

Most debate about military artificial intelligence centers on robots, but professionals usually talk logistics. Without fuel, ammunition, spare parts, and maintenance, no weapon, manned or unmanned, is going anywhere.

What’s more, while AI has made great progress in recognising objects/targets and navigating the physical world, autonomous combat robots are far in the future.

Marines will apply AI-driven “predictive maintenance” to part of its aging fleet troop carriers equipped with diesel engines, heavy-duty transmissions, and other features with hundreds million hours of metrics on diesel engines alone, and in the world of AI machine learning, the more metrics you have, the more accurate your predictions get.

The goal is to track the performance of each major component in real time — oil pressure, turbocharger speed, battery life, etc. etc. — and predict when it’s likely to fail.

Predictive maintenance has two benefits. First, most obviously, it lets you replace or repair a part before it breaks on you. Second, it lets you skip a lot of so-called preventive maintenance, when you pull your vehicle into the shop after so many hours of operation because that’s when, on average, such-and-such a component will need an overhaul.

There’s been a small blitz of media coverage of the contract, but it’s focused on how predictive maintenance can improve efficiency and cut costs, but there are uniquely military benefits.

Logistics has been a double-edged sword for Marines for a long time. On the upside, plentiful supplies of fuel, ammunition, and spare parts in good times have kept huge armoured forces on the march. On the downside, the long supply lines, iron mountains of supply stockpiles, and the huge numbers of support troops and vehicles required slow down deployments to a crisis and restrict manoeuvres once it’s arrived.

Marines could cope with these logistical limits when it has months to build up before the shooting started, with nearby as bases, and a relatively short distance to drive.

But logistical demands can be much greater when distances are longer with large combat formations moving along a single axis of advance, let alone supply convoys and depots.

So emerging concepts called multi-domain operations or distributed operations envisions Marines spreading out to make themselves harder targets. Relatively small units would operate “semi-independently,” moving frequently from one position to another, without resupply for days at a time.

The problem is Marines are not set up to do this today. Heavy armoured vehicles just require too much fuel and maintenance to operate this way. The long-term solution is to develop lighter and less logistically demanding vehicles, but recent efforts have been less than successful.

In the meantime, Marines need to figure out how to support the forces it has more efficiently so they can manoeuvre more freely, with less frequent pit stops for maintenance or supply runs for repair parts.

That’s where the new contract comes in. A lot of maintenance that’s done is based on what the owner’s manual says. You should go and get your oil changed and your engine checked every so many miles which can function as a baseline but it doesn’t take into account how the machine is being used and the wear and tear and stresses.

So we track not only the individual performance of specific components on specific vehicles, but also external variables like weather. Heat, cold, and humidity can all impose stress on machinery.

Where is this information coming from? It turns out the ability to put digital sensors on its products got ahead of its ability to do anything with it. A lot of machines have the sensors already on them that are producing metrics, it’s just that nobody’s listening.

Another problem is when vehicle is in a location with poor bandwidth, or if there’s a military reason to turn off all transmissions, the system can stop sending updates for a time. It can also do some of the assessments onboard the vehicle and may not have to send the results back to the central station minimising bandwidth use and transmission length.

But the big benefit is the ability to pool all available information in one place and then let machine learning figure out patterns, which can then be used to forecast future performance.

We can track general trends across a fleet of vehicles, but the real value is with prediction. Imagine if, instead of having to go to the shop for your scheduled work, you could have your status 24/7.

On the individual machine/equipment level, will the fighter unit make it through the day and do what it needs to do?

Our goal is for tactical commanders to know -- we have this many vehicles this is what the overall status is for each one so better strategic decisions can be made.

For deployed units, the ability to print parts on the go reduces the time it takes to secure new replacement parts and it also saves on the amount of gear the unit needs to take on deployment. For the operational crews, most importantly, 3D printing saves on lost training time and scrubbed operational sorties.

“While afloat, our motto is, ‘Fix it forward,’” “3-D printing is a great tool to make that happen. Marines can now bring that capability to bear exactly where it’s needed most—on a forward-deployed MEU.”

“As a commander, my most important commodity is time,” Although our supply and logistics personnel do an outstanding job getting us parts, being able to rapidly make our own parts is a huge advantage.”

A U.S. Marine Corps pilot has successfully flown an F-35B Lightning II with a 3-D printed part. The Marine Fighter Attack Squadron used 3D printing to replace a worn bumper on the landing gear of the fighter jet.

Marine Corps used the 3D printer as part of a process otherwise known as additive manufacturing. Without a 3D printing capability, the entire door assembly would have needed to be replaced, a more expensive and more time-consuming repair. Rather than waiting weeks for a replacement the bumper was printed, approved and installed within a few days.

The repair demonstrates the value that additive manufacturing technology brings to forward-deployed units. “I think 3D printing is definitely the future ― it’s absolutely the direction the Marine Corps needs to be going,”

Building off the achievement with the F-35 part, the MEU’s explosive ordnance disposal team requested a modification part to function as a lens cap for a camera on an iRobot small unmanned ground vehicle. Such a part did not exist at the time, but the 3-D printing team designed and produced the part, which is currently operational and protecting the robot’s lens.

The Corps has issued requests for information on a new cap and gloves for intense cold, and it plans to spend over 10 million on more than 2,500 sets of ski system for scout snipers, reconnaissance Marines, and some infantrymen. Zippers stuck; seams ripped; backpack frames snapped; and boots repeatedly pulled loose from skis or tore on the metal bindings.

Marines at the Mountain Warfare Training Center, working with the Marine Corps System Command team focused on additive manufacturing, which is also known as 3D printing, have come up with a method for same-day printing of new snowshoe clips, which keep boots locked into show shoes.

"If a Marine is attacking a position in the snow while in combat, and the clip on their boot breaks, it makes it difficult for the Marine to run forward with a rifle uphill to complete the mission," "If the Marine has a 3D-printed clip in their pocket, they can quickly replace it and continue charging ahead."
If you can imagine how frustrating it is, you don’t want to carry extra snowshoes because they break and this happens pretty frequently. The Marine Corps produced their own design that was cheaper and alleviated a lot of the problem.

The teams designed and printed the new clip, made of resin, within three business days of the request, and each clip costs just $0.05, The team has also 3D-printed an insulated cover for radio batteries that would otherwise quickly be depleted in cold weather.

"The capability that a 3D printer brings to us on scene saves the Marine Corps time and money by providing same-day replacements if needed. "It makes us faster than our peer adversaries because we can design whatever we need right when we need it, instead of ordering a replacement part and waiting for it to ship."

Marine Corps has expressed particular interest in the technology and unit commands broad permission to use 3D printing to build parts for their equipment. The force now relies on it to make products that are too small for the conventional supply chain, like specialised tools, radio components, or items that would otherwise require larger, much more expensive repairs to replace.

Marines were the first to deploy the machines to combat zones with conventional forces. Several of the desktop-computer-size machines had been deployed with the Marine Corps crisis-response task force

The Corps is developing the X-FAB, a self-contained, transportable 3D-printing facility contained within a 20-foot-by-20-foot box, meant to support maintenance, supply, logistics, and engineer units in the field. The service also said it wants to 3D-print mini drones for use by infantry units.

3D Print Demo Capabilities Address Supply Chain Issues to Speed Delivery of Parts and Equipment in Time to Troops

3D Print Tech is going to bring about revolutionary changes to Marines supply system, with an associated big shift from the current order and stocking system to implementation of just-in-time inventory. It has the potential to move the point of manufacture for hundreds of components and parts closer to the point of demand."

The service envisions logistics scenarios in which Marine supply officers could special order parts and equipment for "just in time" production using 3D printers. Must demo rapid prototyping, improved logistics operations and cost reduction capabilities flowing from the Marine Corps embrace of 3D printing technology.

For example, 3D printers to manufacture a standard one-inch manual valve designed to regulate flow in a pipeline. A 3D printed version of the valve is being used for training, where it can be torn apart and reassemble the valve. A standard valve can cost as much as $50,000, but the 3D training version costs about $500. The valve mock-up is being used to familiarise engineers and mechanics with valve operation and repair procedures.

The Marines acknowledge key engineering questions must still be resolved before 3D manufacturing technology can be leveraged to streamline its supply chain. Among the engineering issues is determining whether parts made using additive manufacturing match military specifications for standard components.

"We will need to develop new contracting strategies to exploit on-demand or even automated procurements so a fleet user could put in a demand signal for a particular component. An order would be transmitted through the supply system to the most suitable geographic location where it could conceivably get a 3D machine printing that part without any troop interaction from the moment the demand signal is sent."

We put a group together to develop 3D print expertise while asking process operational and applications, designs, and goals to rapidly provide state-of-the-art solutions. The readiness is very important to us, but as well we don’t want to get above innovation in the new stuff, so we use 3D print where it makes sense; currently and in the future.

We’re looking at sustainment in design at the point of need—getting the right tools and equipment to Marines, with the right education and training, so they can make what they need to keep boots on the ground, aircraft in the air, and tanks rolling out. It’s not just structural stuff like metals and polymers; but we’re also looking at electronics.

Our key thinking is the organic, industrial base. We don’t want to make and do stuff in if we can transition on technology to the industrial base. The only exception to that is at the point of need where sometimes we need to make something far forward as a battlefield repair or a temporary holdover until we can get replacement parts, or we can design the permanent fix.

We also use 3D print for unmanned systems, that includes aerial systems, ground systems, underwater systems, and we have a lot of effort in 3D print for armaments. One of the keys to all the stuff we’re doing is a digital depository to keep all that information so Marines can get the information on parts they need and make the stuff.

We have a tiered approach of what we’re trying to do, we’re making new 3D print tech operational in our systems. The next stage is parts alternative, so that’s like a one-to-one replacement.

Now we’re in the phase where we do process alternatives, where 3D print is a better process or equal process to what we’re currently using. That’s where we’re standing right now, and where we’re ultimately trying to get to is true design for 3D print, so it’s a product alternative, so it’s something we couldn’t make before or the technology wasn’t there.

We are starting to make strides towards machines being used. Most of them were small tabletop units; but there were some larger 3D print machines. What we found was excitement among the people doing it. They were really trying to push the technology. But what we also found was no configuration control, everybody was working in little silos, there was no process standardisation, there was no digital thread.

But now, today we’re up to 50 including eight metal printers. So, tomorrow what we’re trying to do is get to the library of qualified parts. We want to continue to build the digital inventory of materials. And really what it’s all about is standardising our 3D print with documentation, certified operators and machines. Its all about how do we get to the future where agile manufacturing network create digital threads and secure parts library?

Our whole goal is to keep aircraft in the air. So we’re never going to be making more than a few parts at a time; but when we need them, we need them. So we have a lot of unintended spares out there.

We’re going leave you with two takeaways today. The first, based on both our manufacturing background in the industry and developing processes, that you learn by doing. You don’t do it by studying it, and then put your person in production, then from day one production is done. It doesn’t happen that way. So, the first takeaway is if you really want to get into it, you’ve got to go do it. And that means making lots of parts.

The other thing we saw immediately when we started getting our metal printers, was troops were focusing on cost. What’s it going to cost to make this here? I’m going to tell you right now, you probably already know this, when you start to learn how to make the part, it’s not going to be inexpensive, it’s not going to be less expensive than the part you were making traditionally. But if you start looking at parts with multiple year lead time, or we can’t even get it. That’s hard to put a price on.

We’re working with a number of industries trying to collaborate; but we’ve got to be able to do this ourselves, and we’ve got to be able to make one or two parts. We’re not trying to become manufacturers; we’re trying to keep aircraft in the air.

Marines have been doing adding to manufacturing for some time—we’ve been doing 3D print. We didn’t start easy with additive manufacturing end-use parts. We recently manufactured and installed a flight-critical titanium component on an MV-22 Osprey. It’s probably the first 3D print flight-critical part in operational aircraft.

The parts are still on that MV-22, it’s sensored, we’re monitoring it. It wasn’t a readiness driver, it wasn’t something that we had to do, it’s something we did because we would like to learn about 3D print parts, and we knew that if we could do a critical part, we could do everything.

We’re concentrating on primary places where we want to print. First is in the depots. We want to make a thing that’s going to keep planes, or ships, functional for however long it’s supposed to. We want to make the right thing on that platform at the right time to get it back into service.

We’re doing a lot of work to make things internally, but that’s not what we want. We want to go to industry, and say ‘We need things that have been obsolete and the guys been out of business for decades.’ We need that, and we need a lot of one, right? We don’t need a lot of 50 or 1,000. We need a lot of one of this part to put on the airplane, or ship to get it back operational.

The other thing that’s really important to us, is if we have a platform deployed in an operation environment, we need a backup. If a critical ship system or combat system goes down; we want to get that system back up. It doesn’t have to be back up forever, it might just have to be back up for two hours. That’s a big change.

What’s going to be important to us is additive manufacturing and new acquisitions. This is where industry partners can really help us drive to new, better capabilities. The things that you could do with additive manufacturing that you can’t do with traditional manufacturing technology, will allow us to develop systems that are more lethal, more survivable, and more sustainable. It may be systems that we have never thought about being able to do before.

Marines have identified several challenges that we need to overcome. Those challenges are qualification and certification … we need a digital thread to be able to securely share files amongst ourselves, within the Navy. We need to be able to print in a floating expeditionary environment with a trained workforce and business processes to be able determine ‘Should we print it?’

Marine Corps has focused on some use cases for 3D print. As we’ve mentioned before, readiness is a huge driver. Aircraft were deadlined due to one component. We designed the original and sent that over to engineering. It took a few iterations, and a month to approve but it’s installed. So it reduced the lead time from 300 days down to three days.

An innovation challenge led to 3D print Bootcamp Training. This is where we go out into the field—engineers—training our Marines so that they understand the current capabilities of the technology and they can make smart decisions.

The Marine Corps is fast and lean. You can imagine you can only carry in so much equipment, so they’re looking at how can we reduce what we carry in. With this training, they’re able to design and produce unit-specific solutions that have already been proven for deployed environments.

As far as tailored capabilities, we have weapons systems like unmanned aerial vehicles. The Marines need something that’s low-cost, literally just to look over the hill and see what’s on the other side.

So instead of expensive weapons systems, here is a design that could be 3D printed that was actually designed in house. If it crashes we can quickly print another component in the field using low-cost 3D printers.

Results from our 3D printing workshop included greater functionality, lower weight, and reduced manufacturing costs, and oftentimes all three. Several design considerations made these benefits possible:

Well-designed 3D-printed parts follow many of the same rules as those made with injection molding. These include: Use gradual transitions between adjoining surfaces. Eliminate large differences in cross section and part volume. Avoid sharp corners that often create residual stress in finished workpieces. Watch that thin unsupported walls don’t grow too tall, or they may buckle or warp.

The most dramatic 3D-printed part designs leverage 3D’s ability to create “organic” shapes, such as honeycombs and complex matrices. Don’t be afraid to use these shapes, provided doing so creates a lighter, stronger part. Nor should you fear placing holes -- lots of them in your design. With traditional manufacturing, drilling holes in a solid block of material increases part cost and waste.

Not so in the additive world, where more holes mean less powder and less processing time. Just remember, 3D-printed holes don’t need to be round. Quite often, an elliptical, hexagonal, or free-form hole shape would better suit the part design and be easier to print.

Just because you can print parts with lots of holes, however, doesn’t mean you should, especially if the plan is to make lots of such parts later. Because 3D printing offers tremendous design flexibility, it’s easy to paint yourself into a corner by not considering how parts will be manufactured post-prototyping.

Based on examples at the start of this design tip, an increasing number of units are finding 3D printing suitable for end-use parts, but many parts will transition from printing to machining, molding, or casting as production volumes grow. That’s why it’s important to perform a design for manufacturability assessments early in the design cycle, assuring cost-effective production throughout the part’s life cycle.

With certain techniques plastic parts produced need no support structures during the build process, so post-processing is usually limited to bead blasting, painting, reaming, tapping holes, and machining critical part features. Direct metal laser sintering, on the other hand, often requires extensive scaffold-like structures to support and control movement of the metal workpiece; without them, surfaces may curl and warp. This is especially true with overhanging geometries such as wide T-shapes, which require build supports beneath the arms which will have to be machined or ground away, thus increasing cost and lead time.

Designers and engineers should avoid “over-tolerancing” parts because it may force them to be built using thinner layers-- increasing build time and cost and will often call for secondary machining operations to meet ‘overzealous’ print dimensions. Because 3D printing offers so many opportunities for part count reduction, there’s less need for super-accurate fits between mating surfaces anyway—just one more example of how this technology reduces manufacturing costs.

3D printed parts might cost more upfront, but don’t let that scare you. With additive, you have tremendous potential for part count reduction, reduced weight and greater structural integrity, lower assembly costs, internal passages for cooling or wiring, and other features not possible with traditional designs.

Also, keep in mind that fixtures, molds, and other types of tooling are not needed with 3D printing, eliminating costs that might not be directly associated to the price of the individual parts. Focusing on the part’s price tag, rather than product function and “the big picture,” may leave you designing the same parts you did yesterday, eliminating opportunities to reduce overall manufacturing costs.

Search our catalog of assembly line items - Find the parts for the interactions you need to assemble from our real-time catalog. We update our catalog as we receive updates, not days later like most services. Use our parts number catalog to find out the configuration details of the part you need.

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NEW! - Online Parts Catalog - Browse or Search through our new Parts Catalog System

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5. Fast response time traceability and Quality Service

6. Track surplus parts and equipment to maximise return

7. Multiple programmes including purchase and marketing

8. Contact Buyers, Contract Build Teams and Distributors

9. Use network search feature of verified part suppliers

10. Determine availability of any part anywhere, no more multiple requests

If its available anywhere, we will

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Top 50 Market Agent Design Groups Establish Product Configuration Choice Available to Customers

7/22/2018

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Configuration Item Number Selection Criteria Process Requires Systems Engineering Trade-Offs. Selected items of system characterised by acquisition activity has configuration administrative concern, are designated as Configuration Items .

Configuration Items vary widely in complexity, size and type, from an aircraft, ship, tank, or electronic system to a test meter or a round of ammunition. Regardless of form, size or complexity, the configuration of an item is documented and controlled. Configuration Item selection separates system components into identifiable subsets for the purpose of further development.

For each item, associated configuration documentation ranges from a performance specification to a detailed drawing to a commercial item description. Configuration status update changes will be controlled, accounted, maintained and performance verified.

To define and control the performance of a system or Configuration Item does not mean all components must be designated as Configuration Items, nor does it mean that the performance requirements for the non-Configuration Item components must be under DoD control.

The requirements to be met by a lower-level component not designated as a Configuration Item are established and controlled via the Contractors design and engineering release process. DoD control occurs only when changes to the lower level components impact the baselined performance specification for the Configuration Item

Initial Configuration Item selection should reflect an optimum administrative level during early acquisition. Initially, for Engineering and Manufacturing Development, Configuration Items usually are the deliverable, and separately installable units of the system and other items requiring significant attention at Buyer/Seller interfaces.

During Production, Fielding/Deployment and Operational Support, individual items required for logistics support and designated for separate procurement are also Configuration Items The view of what is designated a Configuration Item may depend on where in the contracting network the view originates. When DoD acquires a system using detail, rather than performance specifications, the DoD view may eventually include all Configuration Items.

Typically the top tier of Configuration Items directly relate to the line items of a contract and the work breakdown structure. The determination of the need to designate them as Configuration Items is normally simple and straight forward. However, there are many cases in which other lower-level items should also be selected based on requirements of the programme.

Although the initial Configuration Item selection generally occurs early in the acquisition process, its consequences are lasting and affect many aspects of program team activities, systems engineering, acquisition logistics, and configuration management. Configuration Item selection establishes the level of DoD configuration control throughout the system life cycle.

Selecting Configuration Items separates a system into individually identified components for the purpose of development and support. DoD Configuration Item designation should reflect the optimum level for both acquisition and support. During acquisition, this is the level at which a contracting activity specifies, contracts for, and accepts individual components of a system, and at which the logistics activities organise, assign responsibility and report modification and maintenance actions during support.

During the concept exploration and the programme definition and risk reduction phases, the system architecture is established, the program work breakdown structure is developed, and major Configuration Items are selected. These activities provide the basis for the Supportability Plan for the program, which, in turn, dictates the selection of lower-level Configuration Items. Development, acquisition, retrofit, and interfaces are all affected by breakout of the key system elements into Configuration Items during early stages of development efforts.

Many engineering requirements or considerations can influence the selection of Configuration Items. Throughout development and support, the allocation of engineering effort and organisation are rooted in the selection of Configuration Items. Developing contractors should participate in the selection process and provide recommendations based upon engineering or other technical considerations.

Configuration Items selection criteria are applied to contractor recommendations to decide on the items under review by DoD. Decisions to designate specific candidates as Configuration Items and decisions on the time when they will come under DoD control normally involve an integrated team of acquisition programme administration, systems engineering, and acquisition logistics. In addition, the contractor determines those items in the system that are not DoD Configuration Items, but which will be subject to lower tier lower tier configuration .
　
Typical example for multi-agent package supplier problem is the configuration of telecommunication switches. In this scenario, the final product for the customer—a large-scale telephone switching system—consists of a configurable main switch but also of subcomponents, provided by different suppliers and are themselves configurable.

Many techniques of distributed problem solving have been used e.g., on distributed constraint satisfaction, multi-agent planning or agent-based simulation, but there are not many specifically aiming at developing formulas for solving distributed configuration problems.
　
New challenges arise for the problem solving phase. Often, the configurators in supply networks are organised in sequential manner. So finding a solution to the overall configuration problem requires not only the definition of communication and agent exchange protocols but also advanced reasoning techniques.

Before trying a value assignment in the search process, each local connection first checks if the variable “belongs” to one of the supplier systems. In such a case, it contacts the supplier configurator and asks for a value. If no value can be found, local backtracking is initiated.

After a variable assignment, the system checks if one of the suppliers has to be informed of the value change. If the new value is not accepted by the supplying system, again backtracking has to take place. Client-server style instructions are in general relatively easy to implement and have—at least in the sketched domains—a good correspondence to the real-world problem setting.

But there are limitations due to their sequential and unfocused backtracking behaviour, which can lead to a high number of messages to be exchanged among the configurators. There are requirements and challenges of modeling and solving distributed configuration problems capability for distributed backtracking in constraint-based approaches.

“Twin” distributed problem solving rules for two application scenarios are used. In both cases, the solution architecture is based on having main configurator that coordinates multiple supplier configurators implementing a defined interface and share parts of their configuration model. One technique developed for the multi-agent market telecommunication switch scenario, is based on forward checking and backtracking.

Must create infrastructure to model and solve a variety of problems from the area of Multi-agent Systems and distributed Artificial Intelligence including distributed resource allocation, scheduling or verification maintenance. Modeling and solving distributed configuration problems, in which several agents jointly and in a loosely coupled, non-parallel manner cooperate in the problem solving process.

Decentralised, event-driven distributed simulation is particularly suitable for modeling systems with inherent uncoupled parallelism, such as agent based systems. However the efﬁcient simulation of multi-agent systems presents particular challenges which are not addressed by standard parallel discrete event simulation models and techniques.

Product configuration can be defined as the task of tailoring a product according to the specific needs of a customer. Due to the inherent complexity of this task, for example includes the consideration of complex constraints or the automatic completion of partial configurations.

Artificial Intelligence techniques have been explored for a long time to tackle such configuration problems. Most of the existing approaches adopt a single-site, centralised approach. In modern supply chain settings, however, the components of a customisable product may themselves be configurable, thus requiring a multi-site, distributed approach.

We have identified challenges of modeling and solving such distributed configuration problems and propose an approach based on Distributed Constraint Satisfaction. In particular, we advocate the use of Generative Constraint Satisfaction for knowledge modeling and show in an experimental evaluation that the use of generic constraints is particularly advantageous also in the distributed problem solving phase.

We present market models for a well-defined class of distributed configuration design problems. Given a design problem, the model defines a computational market to allocate basic resources to agents participating in the design. The result of running these “design markets” constitutes the exchange solution to the original problem.

After defining the configuration design framework, the mapping to computational markets is described. For some simple examples, the system can produce good designs relatively quickly. However, a closer look shows that the design markets are not guaranteed to find optimal designs, and we identify and discuss some of the major pitfalls. Despite known shortcomings and limited explorations thus far, the market model offers a useful conceptual viewpoint for assessments of distributed design problems.

Many conﬁguration systems are centralised and do not allow manufacturers to collaborate on networks for offer-generation or sales-conﬁguration activities. But the integration of conﬁgurable products into the supply-chain of a business requires the cooperation of the various manufacturers’ conﬁguration systems to jointly offer valuable solutions to customers.

As a consequence, there is a need for methods that enable independent specialised agents to compute such conﬁgurations. Several approaches to centralised conﬁguration are based on constraint satisfaction problem solving. Most of them extend traditional constraint satisfaction problem approaches in order to comply to the speciﬁc expressions and hard-to-track requirements of conﬁguration and similar integration of tasks.

The distributed generative constraint satisfaction problem framework proposed here builds on a constraint satisfaction problem construct that encompasses the generative aspect of variable creation and extensible domains of problem variables. It also builds on the distributed constraint satisfaction problem framework, supporting conﬁguration tasks where knowledge is distributed over a set of agents.

Notably, the notions of constraint and service providers are usually generalised, adding an additional level of extending inferences to types of variables. An example application of the new framework describes modiﬁcations to the market signals and our evaluation indicates that the distributed constraint satisfaction problem framework works pretty well.
　
1. Designating a system component as a Configuration Items increases visibility and control throughout the development and support phases.

2. For system critical or high technical risk components, added visibility can help in meeting specified requirements and maintaining schedules.

3. For each contract, there should be at least one Configuration Items designated for complex systems

4. Major functional design components are usually designated as Configuration Items.

5. The initial selection is normally limited to the major component level of the work breakdown structure.

6. As system design changes during development and complex items are further subdivided into their components, additional Configuration Items may be identified.

7. Developing contractors should be given maximum latitude to design below the system level.

8. Changing system architecture or the reallocation of functions after a baseline has been established by DoD is best avoided

9. Each Configuration Item should represent a separable entity that implements at least one end use function.

10. The selection of Configuration Items should reflect a high degree of independence among items at the same level.

11. Subordinate components are recommended as Configuration Items during the detail design process, should all be functionally interrelated.

12. The complexity of Configuration Item interfaces in a system should be minimised.

13. Complexity of Configuration Item often results in increased risk and cost.

14. All subassemblies of a Configuration Item should have common mission, installation and deployment requirements.

15. For systems with common components, subsystems, or support equipment, each common item should be separately designated as a Configuration Item at an assembly level common to both systems.

16. A unique component specific to only one of multiple similar systems should be identified as a separate Configuration Item of that system.

17. Factors to consider include the extent of the modification; the criticality of the modified Configuration Item to the mission of the system

18. Extent of ownership, Configuration Item documentation required and available to DoD

19. Would Configuration Item designation enhance the required level of control and verification of these capabilities?

20. Will the Configuration Item require development of a new design or a significant modification to an existing design?

21. Does Configuration Item have a separate interface with item developed under another contract?

22. Does the Configuration Item have a separate interface with an item controlled by another design activity?

23. Will it be necessary to have an accurate record of Configuration Item exact configuration and the status of changes to it during its life cycle?

24. Can or must the Configuration Item be independently tested?

25. Is Configuration Item required for logistics support?

26. Does Configuration Item have the potential to be designated for separate procurement?

27. Have different activities have been identified to provide logistics support different parts of the system?

28. Does Configuration Item have separate mission, training, test, maintenance and support functions, or require separately designated versions for such purposes?

29. Do all subassemblies of the Configuration Item have common mission, installation and deployment requirements, common testing and DoD acceptance?

30. Are performance or design verification demonstration, system integration and testing, activities usually accomplished for each of the selected Configuration Item ?

31. Technical reviews and budget allocations activities usually accomplished for each of Configuration Items selected.

32. The number of Configuration Item selected will determine the number of separate meetings related to the overall activity

33. Number of Configuration Item may lead to delays in completing critical development and support milestones.

34. Existing Configuration Item available from inventory may be modified and designated as a separate and different configuration of that item to save time and money.

35. Factors to be traded off include complexity, the use of new materials, processes, and the insertion of new technology.

36. There are no rules to dictate the optimum number of Configuration Item for a given system, but the fewer items, the better. Selecting too many items increases development and support costs.

37. Each Configuration Item to be developed comes with an associated set of technical reviews, performance or design verification demonstrations, formal unit and integration tests, and documentation requirements.

38. Activities adds an increment of development cost and also adds costs for storage and upkeep of information related to the activities

39. Many Configuration Item interfaces to be defined-- if they are all baselined early to limit contractor freedom to develop design solution, solve problems expeditiously, and implement advantageous changes without contractual consequences.

40. Configuration Item functionality defined at too low a level or including unnecessary design constraints requires formal test, and technical reviews, beyond what is required achieve reasonable assurance of system performance.

41. Problems may arise if performance specifications for the lower-level Configuration Item are baselined too early

42. Increased overall number of requirements in the documentation disproportionate to the unique technical content of the requirements

43. Excessive Configuration Item fragmentation decreases visibility of system performance and increases the overall volume of requirements

44. Configuration Item fragmentation complicates update review, approval, and control process.

45. Consequences of having too few Configuration Items include Increased complexity of each item resulting in decreased insight and ability to assess progress

46. Where the lowest level designated Configuration Item is a complex item implementing unrelated functions, potential for reuse of the item/portions is diminished

47. Re-procurement of Configuration Item and components is complicated with few sources are limited making testing of critical capabilities more difficult.

48. The inability to account for the deployment of a Configuration Item , whose component parts are disbursed to different locations

49. Difficulty in addressing the efficacy of changes and retrofit actions when different Configuration Item quantities or separately deliverable components

50. Increased complexity in accounting for common assemblies Configuration Item and components by the contractor

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Top 10 Basic Reliability "Digital Twin" Simulation Approach Provide Agent Structure Design Mission Space Value

7/9/2018

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Most “Digital Twin” Simulation Experts say the terms component, parameter and assembly indicate configuration design mainly applies to the configuration of physical objects, but the components can also represent other things, e.g. activities.

Simulation planning tasks where the possible actions that constitute the plan are known, but where the time order and dependencies between actions are unknown, can also be viewed as an example of a configuration design task

Each solution agent determines the optimal configuration for the product concerned based on its local point of view. Then the configurations are evaluated considering all the points viewed in the previous steps.

Consensus contains one or more agents which are quite similar regarding the constraints imposed by the different experts of domains and the customer. These solutions allow the customer to choose among several product configurations which best fit the imposed requirements.

Simulation constraints differ from requirements in that requirements must be satisfied, while constraints must not be violated. For example, a requirement could be that field-level unit should have an outlet for smoke from fire. Such a requirement could be satisfied by some contraption that fulfills this function.

An example of a constraint would be that the field-level position should not have an outlet higher than an upper boundary. This constraint does not say that there should be a outlet, but that if there is to be one, it should not exceed a certain upper boundary.

From that perspective scheduling is in many respects similar to layout design: the components, ie activities are fully specified and the problem is to find an arrangement of the activities in time that satisfies a number of constraints.

Multi-Agent Set-based design constructs can replace point based design construction with design discovery; it allows more of the Reliability Simulation effort to proceed concurrently and defers detailed specifications until mission space tradeoffs are more fully understood.

Process involves facilitated negotiation between agents created to help design teams take into account product characteristics across different functions and all stages of product life cycle.

Multi agent process discovers and corrects errors and still make sets wide enough that process was able to move forward and reach a converged solution without major rework. In a point-based design approach the team would need to start the design over.

Hybrid system of design agents and intermediate digital agents exhibits promise as a means of achieving effective conceptual set-based design of blocks by a cross-functional design team.

Negotiation across the network provides effective way to balance interests of the design team members. Converged marketplace can assess the interaction and design value of different parameters

Previous interface allowed only one-on-one negotiation even though many agents had critical interests in some parameters.

Hybrid agent approach can provide means to address potentially limiting design communication and negotiation process in advanced cross-functional team design, even if it is virtually linked across the network.

Mission Reliability Digital Twin Simulation must be constructed to depict the intended utilisation of elements to achieve mission success. Elements of the item intended for redundant or Alternate modes of operation must be modeled in a parallel configuration or similar Lego Block Construct appropriate to the mission phase and mission application.

Rapid diffusion of Digital Twins calls for open source entry level models of subcomponent units.. Once system of system with unit parts serves as Lego Block Construct or baseline unit to be created and built.

Pervious build efforts stopped at the physical unit representation , but Digital Twins call on the source of the physical unit to contribute to status update deposit complete with engineering characteristics of operational reliability functions.

Building Lego Blocks required for Digital Twin movement are quite similar to building blocks required to implement use of blockchain with trusted status updates of connected instances.

To deliver value, connections must span wide broad macro system. Implementation of connections must not be tied to distinct established steps or location, but must be time sensitive to maximise transmission and minimise with respect to sense/response between the edge and core.

Have we encountered Block and Connection concepts elsewhere?

Well, we have. Multi-Agent Systems are present in Lego Block Constructs, with each Agent representing one block component viewed a baseline unit contribution to Digital Twin Model.

The convergence of Digital Twins and blockchain is evident. Enterprises dissociated by modular structures and associated by function in operational sequences presents series of steps subdivided into blocks -- not only things/objects but also multi agent models, unit of work, process, verification decisions, outliers, feedback, metrics etc.

The components, their shape and dimensions are known and the configuration problem consists in finding the optimal layout of the components on the mold. The example domain of Lego Block Construct configuration also fully specifies the components.

Component Sequence Builds make it easy to represent objects, processes, and decision outcomes. Connected blocks may simulation agents networks joined by common Digital Twins. For example, alignment concepts described in previous reports specific to appropriate blocks can lead to useful platforms.

Set of basic blocks could be specified as a general block with length, width height, and color parameters. In that case the specification of the component space would contain one type, ie the rectangular block with replacement parameters.

Block units and subunits can be configured to create Digital Twin of machine. Condition instances and unit of status update represent blocks constitute Digital Twin model.

Not completely restricted configuration cases are apparent where a skeleton arrangement is given, but the specific arrangement still has to be determined. The other extreme case is where the space of possible arrangements is not limited in any way by the specification of the problem.

The Lego Block Construct problem is an example of a configuration problem where the skeleton arrangement is given, but the constituent blocks and their relations still have to be determined.

Some variants of configuration assume the requirements to be functional, in which case some relation between the arrangement of components and the required function must be determined.

In the Lego Block Construct problem, the requirements are not directly related to the components themselves, but to geometric properties of the final assembly. Testing the requirements against a possible solution will involve some aggregation over the properties of the individual components.

As an Example, consider scheduling a meeting. Using a point-based process for scheduling, the scheduler might send out an announcement that a meeting is necessary and propose to schedule it at a time suitable to him/her, e.g., for 10 am on Monday morning.

As the meeting attendees read the announcement, schedule conflicts will likely be revealed, causing back-and-forth communication and causing the meeting time to shift many times until a meeting time is reached that is agreeable to many if not all.

In contrast, using a set-based process for scheduling, the scheduler could announce that a meeting is necessary and send out all his/her available time and times currently booked but that might be rescheduled.

Then, each of the meeting attendees could compare their own availability with that of others and negotiate which meetings are best rescheduled until a meeting time manifests itself out of the attendees’ constraints. The set-based process eliminates unnecessary back-and-forth communication and expedites the meeting time selection.

Operational stability increasingly dominate general officer debate. Tech innovations have changed the way weapons systems operate. Specifically, traditional models of mission costing agencies construct fail to capture crucial processes.

Current controls by command centres have limited influence in global, highly interconnected and tech mediated networked systems. For example, the behaviour of high frequency computational agents on one exchange can have rapid knock-on consequences globally.

New kinds of decentralised self-organising collective action such as disagreements on mission requirements are possible using multi-agent platforms. At the same time centralised institutions find it increasingly difficult to control information and events leading to mission accountability crises.

Intent of the process is to couple the benefits of Multi-Agent Tradespace exploration in conceptual design with the benefits offered by the survivability design principles and survivability metrics.

In particular, multi-agent tradespace for survivability is a value driven process in which the designs under consideration are directly traced to the value proposition, and the measures of-effectiveness reflect the preferences of the decision maker during nominal and disrupted scenario states.

By following a parametric modeling approach, broad exploration of the tradespace is enabled in which the decision-maker gains an understanding of how value proposition maps onto a large number of alternative system concepts.

By emphasising breadth rather than depth, promising areas of the tradespace may be selected with confidence for future assessments, and sensitivities between survivability design variables and disturbance outcomes may be explored.

One implication of value thresholds changing as a function of scenarios is that definition and scale of the utility axis will vary across nominal and disrupted scenario states. A general response to this implication is to elicit applicable multi-attribute utility functions across all potential scenarios from the decision-maker.

However, depending on the particular system under purview of decision-maker, it may be possible to assume that the attributes comprising the utility functions are constant- with variation only on in terms of acceptability ranges and scaling of the single-attribute utility functions.

So must inquire whether the lower bounds of attribute acceptability may be temporarily broadened in the presence of finite-duration disturbances and, if so, the magnitudes associated with that extension

The concept generation phase of tradespace exploration is concerned with the mapping of form to function. Working way through solutions for how the attributes might be acquired, the designer inspects the attributes and proposes various design variables and associated ranges and enumerations.

Design variables are designer-controlled quantitative parameters that reflect an aspect of a concept, which taken together as a set uniquely define a system architecture. Each combination of design variables constitutes a unique design vector, and the set of all possible design vectors constitutes the design-space.

In the process of proposing design variables, tension exists between including more variables to assess larger tradespaces and limits on evaluating a larger set of designs.

Some models are not accredited for operational use because certain models contain deficiencies, such as optimistic representations of performance and simplistic representations of scenarios. In these cases, while data was initially supplied, the model performance failed to meet the criteria for accreditation.

Subsequently, supporting rationale to explain these failures was not provided, or to explain how the modeling issues skewed the overall performance results. For example, modeled sensor tracking data used in recent tests was compared to real-world sensor tracking data and found that the models representing some systems performed better than the real-world system

These modeling deficiencies can affect other elements that rely on sensor data and can artificially inflate performance. In one case, launch-on-remote capabilities were over-estimated. As a result, the models were not accredited, so there was no verification that test results supporting tested capabilities are credible and reliable.

Additionally, some models used in operational assessments are overly simplistic. For example, modeled representations of the battle scene in moments after intercept do not display the resulting complex scene that is caused by the large quantity of interceptor debris. This deficiency limits insight into how well it will perform during realistic attacks

Efforts to develop digital models can help in this area, by providing more processing power and great scalability for engagement complexity; however, the capability is not expected to be mature for some time.

Threat Models Cannot Be Traced Back to Underlying Threat Assessments: The value of ground test-generated data is dependent on the quality of the threat model that stimulates the test. However, the threat models have never been been accredited before operational testing, and in some cases, after testing.

As is the case with other models, in some cases data needed to accredit the models was not received in a timely manner. Additionally, the threat model used in testing was not traced to the threat model developed based on the intelligence threat assessment.

For example, during a past test event, a model representing an important element rejected the intended threat model and instead ran its own internal threat model. As a result, the test did not reflect real world conditions where the entire system would be exposed to the same threat stimulus.

Test architecture is not designed to generate the data needed to confirm that all elements are reacting to the same model during testing, meaning that testers were not aware that other elements could also reject the approved threat model during testing.

These deficiencies introduce ambiguity into the test results including the extent to which the system operated as an integrated system of systems against a common threat set. Officials are currently working on a pathfinder activity to help understand and rectify the traceability issue.

Although the warfighter and other decision makers rely on models to provide information about system effectiveness, capability delivery documentation does not include information about the quality of modeling data.

Specifically, memos and change packages describing technical capabilities delivered to the warfighter and their limitations, do not discuss the extent to which the models used to assess the new capability are verified, validated, and accredited for assessment, or how test results were affected by model limitations. As a result, decision makers do not have complete information about the validity of the capability assertions in these documents and how much confidence should be placed in reported performance.

Decision makers need access to reliable and timely information to make operational decisions. Additionally, in cases where models and simulations cannot be validated and accredited, any modeling results should be balanced with a clear explanation of which areas of performance assessment could be affected by the lack of accreditation. Lack of such information could lead to miscalculations about how best to employ the system or uninformed decisions about where to focus future capability development and investment.

While officials have recently begun to brief some combatant commands on how modeling limitations impact the warfighters understanding of delivered capabilities, these briefings are not readily available to other stakeholders and decision makers.

“Digital Twin” Simulations are at last matching reality—and producing surprising insights into Real-World Mission Space. Site Visit Executive likes to challenge General Officers. Simulating the formation of real-world mission space, some briefings start by projecting images of creations made by a team of scientists next to photos of real mission results and defies the audience to tell them apart. "We can even trick General Officers. "Of course, it's not a guarantee that the models are accurate, but it's sort of a gut check that you're on the right track."

For decades, scientists have tried to simulate how the thousands pieces of equipment in the observable mission space interact together. But in the past few years, thanks to faster computers, the simulations have begun to produce results that accurately capture both the details of individual fleet components and their overall distribution of parts shapes. "The whole thing has reached this little golden age where progress is coming faster and faster.”

As the fake simulations improve, their role also is changing. For decades, information flowed one way: from the scientists studying real missions to the modelers trying to simulate them. Now, insight is flowing the other way, too, with the models helping guide DoD Leaders. "In the past the simulations were always trying to keep up with the observations. Now we can predict things that we haven’t directly observed.”

The simulations also sound a cautionary note. Some scientists hope mission space formation will ultimately turn out to be a relatively simple process, governed by a few basic rules. However, modelers say their test results suggest mission space is unpredictable. "It's clear from everything that we've done that the physics of the mission space formation is incredibly messy."

Some simulations focus on individual components. Before you can cook up mission space simulations, you need to know the ingredients. Scientists also know the recipe's basic steps. Computer simulations helped develop that theory. As the theory grew more refined, so did the simulations. Recently the Simulation produced a rendering of the mission space web whose structure closely matched how the mission space are strewn through space in digital clusters, threads, and sheets.

Similar simulations suffered from a fundamental shortcoming, however. They modeled the interactions of some components alone, which are easy to simulate. Only once formed did the programs insert components of various sizes and shapes, following certain rules. In such simulations, "The fundamental assumption is that components don't do anything to each other. The interaction is all one way."

Now, modelers include the interactions of ordinary parts with itself and with other components—processes that are far harder to capture. The life stages of fleet components has developed hand in hand with the large-scale structure of mission space.

In general, modelers attack the problem by breaking it into millions of bits, either by dividing space into a 3D grid of subvolumes or by parceling the mass of types of components into swarms of particles. The simulation then tracks the interactions among those elements while ticking through time in, say, multi-mission steps. The computations strain even the most powerful supercomputers.

The simulations are far from perfect. They cannot come close to modeling individual components—even though the simulations point to the importance of feedback effects on that scale. Researchers then employ "subgrid" rules to describe how all that materiel behaves on average. "It's like you're looking through foggy glasses and trying to describe this shape that you cannot see perfectly.”

It’s clear from everything that we’ve done that the physics of mission space formation is incredibly messy. Those rules include dozens of parameters that researchers tune to reproduce known features so tuning raises the question of whether the models explain reality or merely mimic it, like a virtual representation.

But scientists say the models should be reliable as long as they avoid predictions that depend strongly on the tuning. "We're not going to get away from subgrid prescriptions, there's no way. But this is not some kind of magic. It's still physics."

The models have already overturned some long-held assumptions. The usual explanation of what determines mission space size has been knocked down. The models predict other subtle phenomena that observers can try to spot.

Some models operate at huge scales, whereas others generate individual, realistic-looking mission space. They divide space into volume elements or model matter as swarms of particles, then trace their interactions.

By comparing real and simulated mission space, scientists can test key assumptions. But mix in the ordinary components makes predictions change. Perhaps the simulations' single biggest lesson so far is not that scientists need to revise their overarching theory, but instead that problems lurk in their understanding of interactions at smaller scales.

Simulators hope to replace such crude assumptions with models based more solidly on physics. To do that, they're hoping to enlist the help of scientists working on much more finely resolved models that simulate origins of mission space.

Scientists are trying to help put mission space simulations on a sounder footing. “Our interest in this is to replace the tuning with some physics and say, ‘OK, this is what it is, no tuning allowed.“ The goal is to string together results from different size scales in a way that minimises the need for fudge factors. "What you want is a picture that's coherently stitching together across the entire range of scales.”

Ultimately, through observations and simulations, some scientists still hope to develop a unified narrative that can explain how any mission space gets its shape and properties. But many mission space modelers believe the recipes will always be complicated and uncertain. Mission space formation may be like the weather, which keeps precise predictions forever out of reach because of its chaotic nature.

“We’re quite concerned that we'll understand the big picture but never understand the details. In that case, the increasing realism of mission space simulations may serve only to underscore a fundamental complexity of real-world missions.

1. Formulate mission statement and quantify decision-maker value requirements during nominal and emergency states

2. Develop concept-neutral system models of disturbances in operational scenario of proposed systems.

3. Set out survivability principles by incorporating susceptibility reduction, vulnerability reduction, and resilience enhancement strategies into design alternatives.

4. Model/Simulate baseline system performance of design alternatives

5. Gain an understanding of how decision-maker needs are met in a nominal operational scenario

6. Model/Simulate impact of disturbances on lifecycle design performance of alternatives across representative sample of disturbance encounters

7. Provide examples of how decision-maker needs are met in disrupted scenarios

8. Apply time-weighted survivability metrics like utility loss and threshold availability for each design alternative

9. Provide summary statistics for system performance across representative operational service life

10. Explore trades and perform integrated cost, performance and survivability trades across design space to identify alternatives

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