The Digital Twin changes that with its ability to model and simulate digitally. Downstream functional areas can influence design because working with digital models in the create phase is much cheaper and faster and will continue to move in that direction.
Building Blocks required for Digital Twin manoeuvre 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 mission space. 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 mission space.
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.
Component Sequence Builds make it easy to represent objects, processes, and decision outcomes. Connected blocks can support 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.
Valid operational results based on capacity prediction have been designed to develop new mechanistic route modules. During this process, the transition installation instances between any Two equipment upgrade/repair digital Twin states can be represented by considering the conditional probabilities in sequential series.
Previous Systems Engineering models include the Waterfall Model and the Spiral Model . What these models have in common is a sequential perspective.
The Waterfall model is clearly sequential as the sequence flows from design to operation. Waterfall is very simple to understand and use. In a waterfall model, each phase must be completed before the next phase can start. Development activity is divided into different phases and each phase consists of series of tasks and has different objectives. Waterfall model could be implemented efficiently to design a traffic signal system.
The Spiral model reflects the same, although there is an iterative aspect to it. Spiral model is a combination of sequential and prototype model. This model is best used for large projects which involves continuous enhancements. There are specific activities which are done in one iteration or spiral where the output is a small prototype of the larger system. The same activities are then repeated for all the spirals until build is complete.
Other models involve deconstruction and push down of requirements to the component level and a building back up from components to the complete system.
While these are conceptual models, the messy reality of systems development is that the target forward flow from inception to system is simply an ideal. What actually happens is that there is a great deal of determining that the system as designed does not really deliver the desired behavior, cannot be manufactured, and is not supportable or sustainable at the desired cost levels. Even when the design goes according to plan using deconstruction models, the claim is that it leads to highly fragile systems.
The Digital Twin implementation model attempts to convey a sense of being iterative and simultaneous in the development process. Unlike the Waterfall or even Spiral Models, the downstream functional areas are brought upstream into the create phase meeting considerations of system design. In fact, these areas influence the design.
At first glance, Digital Twin may just seem like a buzzword and that it’s not something that should be associated with things such as engineering or architectural work. However, this is not the case as there are many things that can be done. The best part is that technology is changing every day and the advancements are only making the case to use it stronger.
One benefit being it helps to save both time and money on designs. It prevents going from the design phase to the prototyping phase only to realise that it didn’t come out as intended and end up with wasted money on the design. Virtual reality helps to bridge the gap between these two phases by allowing the user to get the 1:1 scale, see how components sit in the assembly, and ensure that the end design is ready to be manufactured.
The manufacturing of integrated circuits on silicon wafers is a good example of a production process of huge complexity. Many process steps for different types of equipment are required to produce a chip of average complexity. A mix of different process types, i.e. batch processes and single wafer processes, sequence-dependent setup times, very expensive equipment and reentrant flows are typical for this type of manufacturing.
Modern semiconductor manufacturing processes are additionally characterised by a wide and over time changing range of product types and the demand to achieve good delivery performance. Semiconductor wafer fabrication facilities are examples for complex job shops.
A single product moves through the wafer in jobs/lots. Each lot consists of several wafers. The circuits are made up of layers. The recursive nature of the process is one of the main sources of difficulty in planning, scheduling and controlling wafer fabs.
With the time in mind, Digital Twins enable communication of clear design solutions to projects. While looking at a model on a screen or on a drawing, engineers might see different things or not even understand what they may be looking at. Virtual reality gives everyone the same exact view. In some instances, models can be marked up in a way that will aid in adjustments further down the design process.
The entire production process runs itself, without operating personnel, from an autonomous control station. Fundamental to the system is the way all the machines used are networked. The order data are transmitted to the control station, which then prioritises the various build requests and allocates them to an system, for example a 3D print system.. During the build process, the manufacturing status can also be retrieved on a mobile device independent of location.
Once the full production chain has been completed, the quality reports are sent back centrally to the control station. All the data necessary for the production of a “Digital Twin” can be accessed here, so allowing complete traceability, amongst other things.
The 3D printing process has become more and more interesting as a complementary or alternative process to conventional manufacturing techniques. The technology is described as ‘additive’ because of the way in which the process involves the application of layer after layer of thin material, which is then hardened by an energy source. Along with plastics and ceramics, it is also possible to produce metal components in a 3D printing process.
The aim of the pilot project was to develop a next-generation “Digital Twin” manufacturing line which would be able to produce aluminium components for the automotive and aerospace sectors significantly more cost-effectively than is currently possible. The successful outcome of the project means that in terms of the overall production process, manufacturing costs could be reduced by up to 50 percent compared with existing 3D printing systems.
"As far as the aircraft industry is concerned, the aim now is to build further on this expertise and to bring it to bear in other sectors as well.”
The secret lies in a scalable additive production chain, which is fully automated right through to the point where the printed parts are mechanically sawn off the build platform. This means that no manual work is now required at any stage of the process, from the data preparation and central powder supply through to the AM build process itself and including heat treatment, quality assurance and separation of the components from the build platform.
The technical heart of the system is the four-laser system for industrial 3D printing using metal materials. A driverless transport system and robots ensure the smooth movement of the parts through every stage of the production line.
A continuous 3D data string with integrated quality management makes this production system one of the first examples of the benchmark for the future networks. The manufacturing process is completely scalable: the production lines can simply be duplicated to extend the capacity of the plant. This brings the promise of further substantial savings in the future as the numbers rise. Today, the pilot facility is already capable of the automated manufacturing of components to series-production quality standards.
Parts are already being produced on the new technology line: the truck unit, for example, is already using the first replacement part – a bracket for a diesel truck engine.
The 3D printing process is particularly useful in the replacement part sector since, in the event of a tool problem, infrequently required parts can often be reproduced more cost-effectively than with conventional sand or pressure casting processes. The first requests for 3D-printed replacement bus parts in aluminium are currently being examined. The analysis team in the passenger car area is also currently considering suitable potential applications.
Components for new vehicles can be optimised for 3D printing during the design phase, bringing the promise of further advantages in terms of cost. 3D printing also delivers weight benefits, which are of particular interest for electric vehicles.
When it comes to replacement parts, 3D Print brings the advantage, going forward, of saving warehousing costs – because parts can instead be produced ‘on demand’ by the ‘”Digital Twin“, in other words the centralised availability of digital manufacturing data to allow the decentralised production of replacement parts using 3D printing.
“Additive Manufacturing is also suitable for smallest-series production of new limited edition vehicles. Systematic development of the parts specifically for 3D printing means that the production costs can be further reduced and the quality optimised.
3D printing also makes particular sense during the advance development of vehicles. The low numbers required can often be produced more cost-effectively, and faster, with Additive Manufacturing than with conventional production processes.”
This applies just as much for vehicles with a combustion engine as for electric cars. 3D printing is also eminently suitable, for instance, for the production of the integrated base plates that carry the cooling lines for the batteries in electric vehicles.
High product quality comes as standard in the pilot facility. There is a provision for the use of a high-strength aluminium/magnesium/scandium alloy for parts used in the aviation and aerospace sectors.
For the automotive sector, a classic aluminium alloy is used, the material properties of which have been continually improved over the course of the pilot project. The material strength and finish quality, amongst other factors, have been significantly improved compared with when the cooperation started.
Now that all the quality checks so far have been passed with such promising results, preparations are under way for an audit according to the requirements of stringent industry standards. This is one of the prerequisites for the supply of series-production components by contract printing suppliers.
The automation of the entire 3D Print production chain will in future make it possible to manufacture larger batches in series production – with the same reliability, functionality, durability and economic efficiency as conventionally manufactured components.
1. Modeling states of other agents
2. Fusing uncertain sensor data
3. Inter-component robotic communication
4. Deliberative behaviours for pursuit
5. Mixed reactive behaviour
6. Local/global intelligence
7. Pay attention to reward
8. Interdependent resource actions
9. Iterative game play
10. Collective competitive change
11. Deduce intentions through observation
12. Deduce abilities through observation
13. Model as a team with individual roles
14. Depend on others for goal
15. Multi-agent adaptive load balancing
16. Focal points/emergent conventions
17. Agents filling different roles
18. Distributed /Active sensing
19. Generalised partial global planning
20. Query propagation
21. Distributed traffic mapping
22. Planning State
23. Goal communication
24. Negotiation between agents
26. Resource schedule coordination
27. Internal collective commitment
28. Internal collective decommitment
29. Changing shape/ size
30. Grounding meaning via shared experience
31. Legacy systems integration
32. Reasoning about accuracy
33. Training other agents track driving
34. Minimise need for training
35. Market-based techniques
36. Distributed constraints.
37. Generalised partial global planning
38. Learning to choose coordination techniques
39. Query response in information networks
40. Potential commitment states
41. Pre-action/Actual commitment states
42. Collaborative localisation
43. Communication utility/accuracy
44. Grounding meaning via shared experience
45. Legacy systems integration
46. Training other agents track driving
47. Minimise need for training
48. Market-based methods for distributed constraints
49. Query response in information networks
50. Division of independent tasks