Pentagon has come to conclusion that advantage in future battles depends on our ability to fuse vast amounts of information to accelerate our decision cycle to guarantee the success of any mission. Victory in combat will depend on us becoming a Digital Organistion.
Digital initiative encompasses comprehensive changes to how the force gathers, uses and shares data. “We must control and manipulate massive volumes of information to out-think and out-maneuver its opponents. The Digital initiative will ensure all Troops have uninterrupted access to the data they need, where and when they need it.”
“Our goal is to eliminate the time Troops spend building PowerPoint slides to display information needed to make a decision. “We should create or have tools to hook into comprehensive data streams to provide real-time information for rapid, data-driven decisions rather than solely relying on their personal experiences, intuition and interpretation.”
The Digital initiative calls for several connected elements of reform. The Services must develop a modernised information technology infrastructure to serve as a common backbone for data and information flow. It must institute data standards that allow the diverse elements of the force to share data and use artificial intelligence platforms. It is also key to adopt agile business practices to improve effectiveness and efficiency.
“Besides our laser-focus on driving the Network services with greater agility and scale, we’re also ensuring security practices are ‘baked in’ and that Troops are empowered to leverage a global enterprise of data and services.” The Digital Force must also design and use standardised policies and network protocols to ensure the free exchange of data between platforms.
“We are charged with harnessing the power of data for timely decision-making and mission success. With new data management practices, we will improve readiness, increase mission effectiveness, reduce the total cost of operations, improve network security and make rapid, accurate, data-driven decisions.”
Digital Force requires improved day-to-day business operations via application rationalisation, system consolidation, enterprise optimisation and continuous process improvement. These efforts will allow us to continuously identify cost-savings opportunities to directly fund digital transformation efforts.”
Ultimately, the Digital initiative aims to harness the power of the digital era. “We must move beyond antiquated processes, systems and mindsets. We will pursue new ways to leverage technology and institute an institution of innovation and informed risk-taking.”
The Digital initiative focuses on eliminating “antiquated processes” and overhauling how the service maintains, uses and shares data and information, amid difficulties coordinating systems across air, land, sea and network domains to react to the demands of modern combat.
Becoming a Digital Force requires data that is gathered, stored and transmitted in commonly read and processed formats to minimise the delay between receiving, processing and using information derived from multiple systems to meet our tactical and strategic ends.”
“The future DoD digital infrastructure will provide seamless, agile, resilient, transparent and secure infrastructure and services that advance DoD information superiority and simplify information sharing with mission partners.”
The Digital Blueprint looks to improve how data collected by airborne intelligence, surveillance, and reconnaissance platforms is shared, drive the Pentagon toward a greater command of electronic warfare, and turn to quick contracting methods to bring new technology on board faster.
Successful implementation of digital model-based systems engineering solves integration and training problems, as well as reduce the time and burden required to design, vet, redesign, and test and evaluate new planes, ships, weapons and more.
First, program offices need to sit down with operators and understand the requirement on a tactical level: what mission needs to be accomplished, what capability is needed, what threat is being countered, how will the system be used, who will use it, and more. If that information is all included in a computer model, NAVAIR can insert a notional placeholder aircraft or weapon into the model and pass it along to industry to actually engineer.
“Operators want to know our capability has been fully characterised, so not only do they know what it does, but they know what it doesn’t do – equally important to them when they take it into combat.”
“When we deliver that capability to them on Day 1 it’s fully integrated with the environment they’re expecting to utilise it in, which is done poorly, and “give them a system on Day 1 that they can fully train with. Fully train with, 100-percent of the capability we’re giving them.
Currently we’re telling them what it can do, but typically we also go, ‘don’t worry, your training system, your digital simulator software, it’s an iteration or two behind, but it’ll catch up.’ Well, it usually doesn’t catch up because we keep rolling the operational tools but the training tools comes behind.”
“We’ve got a digital model of our threat; we’ve got a digital model of our blue forces, we got environmental models, whether I’m operating in an electromagnetic warfare spectrum or in the acoustic spectrum under the water; it’s all done with digital models.”
It’s important for industry to engineer the new product within that digital model, instead of today’s practice of, “we write a 500-page specification with 20,000 shall-statements, and we give it to industry and go, here, design this. We don’t give them the threat models, we don’t give them the blue force models, we don’t give them that system of systems family model we just built.
If industry can work within that digital environment, then little changes can be made along the way – swapping one sensor for another, for example – without wondering how that change may affect the aircraft’s aerodynamics or its low-observability or other features that today are designed separately on paper.
With a digital drawing in a threat-representative environment, the sensor could be swapped out and thousands of possible engineering solutions Generated until the best one is chosen and be done in a matter of hours or days, rather than the months it would take with today’s processes to make those changes.
Benefits to this type of design effort continue throughout the test and evaluation, fielding and sustainment phases.
For example, in lieu of a paper-based design review, as industry meets various milestones in maturing its design “let’s take those digital models and let’s put them back in the tactical scenarios we developed with the operator back in step 1 and just see how it goes.
What better evaluation or assessment of how the program is maturing than to actually run the current level of maturity of performance that we see in our models through the tactical situations we’ve built with the operators? Because in the end that’s what matters, in the end capabilities-based test and evaluation is about testing the capabilities – it’s not about ensuring industry met every one of those 20,000 specs.
That’s where we spend all our time today during T&E, validating that industry met the specs. The fleet couldn’t care less, the fleet wants to know that the attributes and the capabilities that they’re counting on will be met.”
That sort of constructive testing – all conducted within the simulated environment – could pave the way to eventual virtual testing once the first hardware is delivered, which would then pave the way to eventual live testing in the field with real operators. That flow would make the best use of everyone’s time and allow any problems to be addressed as early on as possible.
Developing and maturing a “digital representation of that system and how it interacts with its environment” would also go a long way in delivering relevant trainers from the outset, rather than today’s simulators without the latest update or inaccurate threat environments.
The digital representation would also help with sustainment efforts throughout the life of the program, as models could help show how the system would hold up over time and in different environments.
“We’re in the process of building that model-based specification, that digital spec for industry right now. When we get that part right, build that spec, now it’s in industry’s hands” to continue to make best use of that model environment.”
We want to learn a lot about model-based engineering with new systems without bogging the program down with new processes and design tools. To supplement that learning effort, Navy and industry could conduct a “Surrogate System Experiment: to help identify potential kinks in the new process to bring this group – representation from the organisations represented in this group today – into a collaborative environment where we can actually build a surrogate program and execute that model, the capabilities-based acquisition model.”
“Find out where the hard spots are, find out where we have to go soft, find out what is that deliverable, what kind of contract will work, where do we have to hand off between DoD and industry, how do we truly make that integrated digital environment work in secure network environment?”
NAVAIR intends to begin implementing the digital model-based engineering concept into any new design, capability upgrade or sustainment program it can, seeking opportunities to learn as quickly as possible, “as opposed to waiting for the big bang on a brand new program.”
Digital model optimisation and Generative design programs were developed to let design engineers seek and evaluate a range of possible design options, based on a mix of time-tested finite element analysis. In some ways, they are the opposite of CAD. Some of them include a limited set of geometry construction tools, but they function primarily as form-Generating engines, sometimes offering not your typical geometry that users may not have imagined.
Recently, the introduction of topology optimisation and generative design elements in CAD has started to blur the line between parametric modeling and Generative design.
Topology optimisation is specific to the exploration of digital shapes, structures and solid geometry. It’s usually associated with aerospace lightweighting projects, where engineers seek ways to reduce material without risking the design’s safety requirements. For example, aircraft engineers may redesign a landing gear to weigh somewhat less, with the same load-bearing capacity.
“Engineers were not trained to formulate the problem. They were trained to find solutions.” Formulating the problem, as it turns out, is the key to generating good geometry.
For engineers getting started in Generative design, they certainly can pick it up faster if they understand how to apply realistic engineering constraints. Moreover, we see a future that is more multidisciplinary, making engineering concurrent. This brings team knowledge to the product ideation phase, which can help to release the burden on the mechanical engineer to define the problem and create the solution with such new methodology.”
Functional Generative design tools enable you to move beyond the boundaries of traditional CAD modeling and harness the power of 3D printing and digitised models.”
Generative design inquiries are usually not yes/no questions--will it break or will it hold? they’re formulated as what questions-- under these conditions, what are the best bracket design options to secure the fuel tank?. As you add additional constraints or parameters such as acceptable weight range for the bracket, preferred manufacturing materials and more the design options change.
“The design process has always been Generative. It’s just that computers have never been part of that Generative process, comparing CAD programs to Generative design programs may be as counterproductive as comparing apples and oranges, because “the two are doing different things. You need enough CAD interoperability to get you going with Generative design.”
“We’ve always had optimisation in our tools, but it’s driven by dimension. For the first time we introduced topology optimisation. This is where, based on loading conditions you supply, the tools takes away materials from the base geometry you supplied, and provides you with an organic, optimal shape.”
The new feature gives you a choice to define whether to produce the part in additive or subtractive manufacturing. “Based on that specification, the tool removes materials very differently for each approach. Because the optimised geometry is digitally modeled, you can use the familiar CAD tools to refine the geometry in the final phase.
There is distinction between Generative design and computational design. “In Generative design, you use the computer to assist you in finding the right design. With computational design, you may design a framework that lets you combine a heavy data set, concepts and ‘build-ability’ factors to guide your design.
A good computation model should include rules to minimise failures. “Let’s say you have an unusual building envelope shape, and you want to explore ways to build it in titanium or glass-reinforced fiber cement. You can build those material behaviours into the computational model, so the tool knows what the maximum or minimum curvatures allowable are.”
Design engineers with less computational skills may prefer a Generative design tool that has built-in rules that automatically exclude forms that cannot be manufactured or constructed. On the other hand, some prefer an open system that engineers or architects can use to build a framework of constraints, to limit the available options to fit the manufacturing or building criteria.
Quite often, what is digitally optimal—geometry with sufficient material reinforcement to counter the anticipated stress in different regions—is impractical to manufacture or produce, either due to cost concerns or the limitations of the production methods available.
Additive manufacturing now gives the option to 3D print certain complex geometric forms that cannot be machined; however, even with AM, there are certain rules about printable geometry.
“Many topology optimised structures have thin walls and complex geometries with lots of overhanging features, both of which may be challenging even for AM. Some of the new Generative design tools take these into account, but most do not—and engineers are not used to making these trade-offs during the design process. Before AM came along, we didn’t even know these trade-offs existed, let alone how to account for them in our design tools.
In some Generative design programs, the manufacturing feasibility rules are part of the Generative formulas; in others, they’re not. If they’re not, it’s up to the design engineers to work with the manufacturing engineers and construction engineers to account for them in the Generative setup. Otherwise, the range of solutions proposed by the tool will most likely include problematic geometric features.
Math deals with the absolute; reality is full of uncertainty and unexpected changes. This means Generative design solution you get is perfect for the problem you’ve defined, but it might not be perfect for the real problem. You may have defined the rocket fuel tank pressure vessel with all the load cases you can think of, but in reality, there may be the effects of sloshing, which you didn’t consider or know how to define. So the optimisation tool didn’t take that into account.”
Before digital optimisation made lightweighting possible, engineers usually over-engineered parts to account for unanticipated load cases. They build parts to be much stronger than necessary, just in case. As lightweighting becomes the norm, over-engineering is now seen by many as a wasteful practice. But is engineering parts with barely enough strength a good idea?
Accessibility to various users’ experience levels is key.
“We have worked very hard to make it as easy as possible to start using Digital Generate. “It’s difficult to predict what the user may or may not understand up front. One thing is clear—after some hands-on demos, a light goes on and they’re excited to rapidly start iterating with Generative design.”
Primary objective of understanding and documenting user constraints is identifying the system/capability requirements of parts critical to the mission. A requirement can be defined as 1) a characteristic that identifies the performance levels needed to satisfy specific objectives within a given set of conditions and 2) binding statement in a supplier contracts.
There are three basic types of requirements: functional, performance, and constraint. Functional requirements identify the necessary task, action, or activity that must be accomplished -- what the system/capability must provide. Performance requirements characterise how well the system/capability must perform a function when subjected to expected conditions. Constraint requirements are subject to the restrictions placed on a system/capability through policy, procedural, technology or interface conditions.
The source of requirements is the customer i.e., purchaser or system/capability to include the acquirer, user, customer, manufacturer, installer, tester, maintainer, User requirements are often not adequate for design purposes since they usually stated in nontechnical terms, ie mission critical function & expectations.
The user requirements become clear, unambiguous, and measurable as they are derived into technical requirements achievable through application of technology. Requirements development/administrative activities include mandating Logistics programs responding to a capabilities or requirements processes desired requirements balance what is acceptable to the stakeholders.
Regardless of acquisition category, DoD must apply a robust systems engineering approach that balances total system performance total ownership costs within the family-of-systems, systems-of-systems context. Industry Partners must execute Systems Engineering Plans for Milestone Decision Authority approvals integrated within the Acquisition Strategy.
Plan must describe the overall technical approach of Industry, including processes, resources, metrics, and applicable performance incentives. It must also detail the timing, conduct, and success criteria of technical reviews.” Systems engineering can be defined as an iterative process of top-down synthesis, development, and operation of a real-world system that satisfies full range of system requirements.
Industry must provide Systems engineering working together on a set of inputs to achieve the desired output where the output is a system/capability that meets the user’s needs and requirements in a near optimal manner.
Systems engineering must account for the entire range of the system/capability acquisition to include development, manufacturing, construction, deployment /fielding, operation, support, training, and verification. Systems engineering ensures that the correct technical tasks are accomplished during the acquisition process through planning, tracking, and activities coordination Lead Systems Engineers are responsible for:
1. Elicit requirements from customers and potential product/service users
2. Validate and prioritise customer/user requirements
3. Define executable and verifiable requirements solutions
4. Isolate/verify balanced and robust solutions that best meet requirements
5. Execute total system design solution to balance cost, schedule, performance, and risk
6. Track decision making events for technical information meet customer requirements
7. Create cost-effective and supportable system throughout asset service life
8. Adopt open systems approach to monitor internal/external interface compatibility for systems and subsystems
9. Establish baselines and configuration control for each phase in engineering process
10. Provide for focus/structure of interdisciplinary teams for system and major subsystem level design.
Digital initiative encompasses comprehensive changes to how the force gathers, uses and shares data. “We must control and manipulate massive volumes of information to out-think and out-maneuver its opponents. The Digital initiative will ensure all Troops have uninterrupted access to the data they need, where and when they need it.”
“Our goal is to eliminate the time Troops spend building PowerPoint slides to display information needed to make a decision. “We should create or have tools to hook into comprehensive data streams to provide real-time information for rapid, data-driven decisions rather than solely relying on their personal experiences, intuition and interpretation.”
The Digital initiative calls for several connected elements of reform. The Services must develop a modernised information technology infrastructure to serve as a common backbone for data and information flow. It must institute data standards that allow the diverse elements of the force to share data and use artificial intelligence platforms. It is also key to adopt agile business practices to improve effectiveness and efficiency.
“Besides our laser-focus on driving the Network services with greater agility and scale, we’re also ensuring security practices are ‘baked in’ and that Troops are empowered to leverage a global enterprise of data and services.” The Digital Force must also design and use standardised policies and network protocols to ensure the free exchange of data between platforms.
“We are charged with harnessing the power of data for timely decision-making and mission success. With new data management practices, we will improve readiness, increase mission effectiveness, reduce the total cost of operations, improve network security and make rapid, accurate, data-driven decisions.”
Digital Force requires improved day-to-day business operations via application rationalisation, system consolidation, enterprise optimisation and continuous process improvement. These efforts will allow us to continuously identify cost-savings opportunities to directly fund digital transformation efforts.”
Ultimately, the Digital initiative aims to harness the power of the digital era. “We must move beyond antiquated processes, systems and mindsets. We will pursue new ways to leverage technology and institute an institution of innovation and informed risk-taking.”
The Digital initiative focuses on eliminating “antiquated processes” and overhauling how the service maintains, uses and shares data and information, amid difficulties coordinating systems across air, land, sea and network domains to react to the demands of modern combat.
Becoming a Digital Force requires data that is gathered, stored and transmitted in commonly read and processed formats to minimise the delay between receiving, processing and using information derived from multiple systems to meet our tactical and strategic ends.”
“The future DoD digital infrastructure will provide seamless, agile, resilient, transparent and secure infrastructure and services that advance DoD information superiority and simplify information sharing with mission partners.”
The Digital Blueprint looks to improve how data collected by airborne intelligence, surveillance, and reconnaissance platforms is shared, drive the Pentagon toward a greater command of electronic warfare, and turn to quick contracting methods to bring new technology on board faster.
Successful implementation of digital model-based systems engineering solves integration and training problems, as well as reduce the time and burden required to design, vet, redesign, and test and evaluate new planes, ships, weapons and more.
First, program offices need to sit down with operators and understand the requirement on a tactical level: what mission needs to be accomplished, what capability is needed, what threat is being countered, how will the system be used, who will use it, and more. If that information is all included in a computer model, NAVAIR can insert a notional placeholder aircraft or weapon into the model and pass it along to industry to actually engineer.
“Operators want to know our capability has been fully characterised, so not only do they know what it does, but they know what it doesn’t do – equally important to them when they take it into combat.”
“When we deliver that capability to them on Day 1 it’s fully integrated with the environment they’re expecting to utilise it in, which is done poorly, and “give them a system on Day 1 that they can fully train with. Fully train with, 100-percent of the capability we’re giving them.
Currently we’re telling them what it can do, but typically we also go, ‘don’t worry, your training system, your digital simulator software, it’s an iteration or two behind, but it’ll catch up.’ Well, it usually doesn’t catch up because we keep rolling the operational tools but the training tools comes behind.”
“We’ve got a digital model of our threat; we’ve got a digital model of our blue forces, we got environmental models, whether I’m operating in an electromagnetic warfare spectrum or in the acoustic spectrum under the water; it’s all done with digital models.”
It’s important for industry to engineer the new product within that digital model, instead of today’s practice of, “we write a 500-page specification with 20,000 shall-statements, and we give it to industry and go, here, design this. We don’t give them the threat models, we don’t give them the blue force models, we don’t give them that system of systems family model we just built.
If industry can work within that digital environment, then little changes can be made along the way – swapping one sensor for another, for example – without wondering how that change may affect the aircraft’s aerodynamics or its low-observability or other features that today are designed separately on paper.
With a digital drawing in a threat-representative environment, the sensor could be swapped out and thousands of possible engineering solutions Generated until the best one is chosen and be done in a matter of hours or days, rather than the months it would take with today’s processes to make those changes.
Benefits to this type of design effort continue throughout the test and evaluation, fielding and sustainment phases.
For example, in lieu of a paper-based design review, as industry meets various milestones in maturing its design “let’s take those digital models and let’s put them back in the tactical scenarios we developed with the operator back in step 1 and just see how it goes.
What better evaluation or assessment of how the program is maturing than to actually run the current level of maturity of performance that we see in our models through the tactical situations we’ve built with the operators? Because in the end that’s what matters, in the end capabilities-based test and evaluation is about testing the capabilities – it’s not about ensuring industry met every one of those 20,000 specs.
That’s where we spend all our time today during T&E, validating that industry met the specs. The fleet couldn’t care less, the fleet wants to know that the attributes and the capabilities that they’re counting on will be met.”
That sort of constructive testing – all conducted within the simulated environment – could pave the way to eventual virtual testing once the first hardware is delivered, which would then pave the way to eventual live testing in the field with real operators. That flow would make the best use of everyone’s time and allow any problems to be addressed as early on as possible.
Developing and maturing a “digital representation of that system and how it interacts with its environment” would also go a long way in delivering relevant trainers from the outset, rather than today’s simulators without the latest update or inaccurate threat environments.
The digital representation would also help with sustainment efforts throughout the life of the program, as models could help show how the system would hold up over time and in different environments.
“We’re in the process of building that model-based specification, that digital spec for industry right now. When we get that part right, build that spec, now it’s in industry’s hands” to continue to make best use of that model environment.”
We want to learn a lot about model-based engineering with new systems without bogging the program down with new processes and design tools. To supplement that learning effort, Navy and industry could conduct a “Surrogate System Experiment: to help identify potential kinks in the new process to bring this group – representation from the organisations represented in this group today – into a collaborative environment where we can actually build a surrogate program and execute that model, the capabilities-based acquisition model.”
“Find out where the hard spots are, find out where we have to go soft, find out what is that deliverable, what kind of contract will work, where do we have to hand off between DoD and industry, how do we truly make that integrated digital environment work in secure network environment?”
NAVAIR intends to begin implementing the digital model-based engineering concept into any new design, capability upgrade or sustainment program it can, seeking opportunities to learn as quickly as possible, “as opposed to waiting for the big bang on a brand new program.”
Digital model optimisation and Generative design programs were developed to let design engineers seek and evaluate a range of possible design options, based on a mix of time-tested finite element analysis. In some ways, they are the opposite of CAD. Some of them include a limited set of geometry construction tools, but they function primarily as form-Generating engines, sometimes offering not your typical geometry that users may not have imagined.
Recently, the introduction of topology optimisation and generative design elements in CAD has started to blur the line between parametric modeling and Generative design.
Topology optimisation is specific to the exploration of digital shapes, structures and solid geometry. It’s usually associated with aerospace lightweighting projects, where engineers seek ways to reduce material without risking the design’s safety requirements. For example, aircraft engineers may redesign a landing gear to weigh somewhat less, with the same load-bearing capacity.
“Engineers were not trained to formulate the problem. They were trained to find solutions.” Formulating the problem, as it turns out, is the key to generating good geometry.
For engineers getting started in Generative design, they certainly can pick it up faster if they understand how to apply realistic engineering constraints. Moreover, we see a future that is more multidisciplinary, making engineering concurrent. This brings team knowledge to the product ideation phase, which can help to release the burden on the mechanical engineer to define the problem and create the solution with such new methodology.”
Functional Generative design tools enable you to move beyond the boundaries of traditional CAD modeling and harness the power of 3D printing and digitised models.”
Generative design inquiries are usually not yes/no questions--will it break or will it hold? they’re formulated as what questions-- under these conditions, what are the best bracket design options to secure the fuel tank?. As you add additional constraints or parameters such as acceptable weight range for the bracket, preferred manufacturing materials and more the design options change.
“The design process has always been Generative. It’s just that computers have never been part of that Generative process, comparing CAD programs to Generative design programs may be as counterproductive as comparing apples and oranges, because “the two are doing different things. You need enough CAD interoperability to get you going with Generative design.”
“We’ve always had optimisation in our tools, but it’s driven by dimension. For the first time we introduced topology optimisation. This is where, based on loading conditions you supply, the tools takes away materials from the base geometry you supplied, and provides you with an organic, optimal shape.”
The new feature gives you a choice to define whether to produce the part in additive or subtractive manufacturing. “Based on that specification, the tool removes materials very differently for each approach. Because the optimised geometry is digitally modeled, you can use the familiar CAD tools to refine the geometry in the final phase.
There is distinction between Generative design and computational design. “In Generative design, you use the computer to assist you in finding the right design. With computational design, you may design a framework that lets you combine a heavy data set, concepts and ‘build-ability’ factors to guide your design.
A good computation model should include rules to minimise failures. “Let’s say you have an unusual building envelope shape, and you want to explore ways to build it in titanium or glass-reinforced fiber cement. You can build those material behaviours into the computational model, so the tool knows what the maximum or minimum curvatures allowable are.”
Design engineers with less computational skills may prefer a Generative design tool that has built-in rules that automatically exclude forms that cannot be manufactured or constructed. On the other hand, some prefer an open system that engineers or architects can use to build a framework of constraints, to limit the available options to fit the manufacturing or building criteria.
Quite often, what is digitally optimal—geometry with sufficient material reinforcement to counter the anticipated stress in different regions—is impractical to manufacture or produce, either due to cost concerns or the limitations of the production methods available.
Additive manufacturing now gives the option to 3D print certain complex geometric forms that cannot be machined; however, even with AM, there are certain rules about printable geometry.
“Many topology optimised structures have thin walls and complex geometries with lots of overhanging features, both of which may be challenging even for AM. Some of the new Generative design tools take these into account, but most do not—and engineers are not used to making these trade-offs during the design process. Before AM came along, we didn’t even know these trade-offs existed, let alone how to account for them in our design tools.
In some Generative design programs, the manufacturing feasibility rules are part of the Generative formulas; in others, they’re not. If they’re not, it’s up to the design engineers to work with the manufacturing engineers and construction engineers to account for them in the Generative setup. Otherwise, the range of solutions proposed by the tool will most likely include problematic geometric features.
Math deals with the absolute; reality is full of uncertainty and unexpected changes. This means Generative design solution you get is perfect for the problem you’ve defined, but it might not be perfect for the real problem. You may have defined the rocket fuel tank pressure vessel with all the load cases you can think of, but in reality, there may be the effects of sloshing, which you didn’t consider or know how to define. So the optimisation tool didn’t take that into account.”
Before digital optimisation made lightweighting possible, engineers usually over-engineered parts to account for unanticipated load cases. They build parts to be much stronger than necessary, just in case. As lightweighting becomes the norm, over-engineering is now seen by many as a wasteful practice. But is engineering parts with barely enough strength a good idea?
Accessibility to various users’ experience levels is key.
“We have worked very hard to make it as easy as possible to start using Digital Generate. “It’s difficult to predict what the user may or may not understand up front. One thing is clear—after some hands-on demos, a light goes on and they’re excited to rapidly start iterating with Generative design.”
Primary objective of understanding and documenting user constraints is identifying the system/capability requirements of parts critical to the mission. A requirement can be defined as 1) a characteristic that identifies the performance levels needed to satisfy specific objectives within a given set of conditions and 2) binding statement in a supplier contracts.
There are three basic types of requirements: functional, performance, and constraint. Functional requirements identify the necessary task, action, or activity that must be accomplished -- what the system/capability must provide. Performance requirements characterise how well the system/capability must perform a function when subjected to expected conditions. Constraint requirements are subject to the restrictions placed on a system/capability through policy, procedural, technology or interface conditions.
The source of requirements is the customer i.e., purchaser or system/capability to include the acquirer, user, customer, manufacturer, installer, tester, maintainer, User requirements are often not adequate for design purposes since they usually stated in nontechnical terms, ie mission critical function & expectations.
The user requirements become clear, unambiguous, and measurable as they are derived into technical requirements achievable through application of technology. Requirements development/administrative activities include mandating Logistics programs responding to a capabilities or requirements processes desired requirements balance what is acceptable to the stakeholders.
Regardless of acquisition category, DoD must apply a robust systems engineering approach that balances total system performance total ownership costs within the family-of-systems, systems-of-systems context. Industry Partners must execute Systems Engineering Plans for Milestone Decision Authority approvals integrated within the Acquisition Strategy.
Plan must describe the overall technical approach of Industry, including processes, resources, metrics, and applicable performance incentives. It must also detail the timing, conduct, and success criteria of technical reviews.” Systems engineering can be defined as an iterative process of top-down synthesis, development, and operation of a real-world system that satisfies full range of system requirements.
Industry must provide Systems engineering working together on a set of inputs to achieve the desired output where the output is a system/capability that meets the user’s needs and requirements in a near optimal manner.
Systems engineering must account for the entire range of the system/capability acquisition to include development, manufacturing, construction, deployment /fielding, operation, support, training, and verification. Systems engineering ensures that the correct technical tasks are accomplished during the acquisition process through planning, tracking, and activities coordination Lead Systems Engineers are responsible for:
1. Elicit requirements from customers and potential product/service users
2. Validate and prioritise customer/user requirements
3. Define executable and verifiable requirements solutions
4. Isolate/verify balanced and robust solutions that best meet requirements
5. Execute total system design solution to balance cost, schedule, performance, and risk
6. Track decision making events for technical information meet customer requirements
7. Create cost-effective and supportable system throughout asset service life
8. Adopt open systems approach to monitor internal/external interface compatibility for systems and subsystems
9. Establish baselines and configuration control for each phase in engineering process
10. Provide for focus/structure of interdisciplinary teams for system and major subsystem level design.
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