Digital Twin of the actual part can then be sent back to the designers to compare what we have manufactured to what the model wants, and even use the actual part model to simulate its impact, digitally, in the final design.
In the case of a 3D printer, we’re building a Digital Twin of a build process and recording the slightest defects, deviations and other build characteristics. With Digital Twins, models will continually be updated with each new build and become ever smarter in recognizing and troubleshooting any potential issues that might arise.
Not only will there be a Digital Twin of the component, showing the internal and external requirements, but also a Digital Twin of the process that made that part; the process parameters, how long did the build take, how many layers were built, were there any issues.. all of these aspects building a digital picture of the part enabling further analysis and confidence in final applications of components.
Next generation of Digital Twins incorporate information from other sensors monitoring the 3D printing process, such as the shape of the pool of metal rendered molten by the laser. In addition, this smart, real-time quality control will not function in isolation.
The power of Digital Twins is their ability to share insights with each other. So you can imagine many 3D print machines sharing unique build insights with each other that makes them each more informed about what to watch for during a build process.
Through the Digital Twin process, you can accelerate the production of mission-critical equipment. Using Digital Twin technology, we’re aiming to rapidly speed up the time that parts could be re-engineered or newly created using 3D printing processes.
The key challenge with 3D printing is being able to additively build a part that mirrors the exact material composition and properties of the original part that was formed through subtractive measures. With operation of mission-critical parts there is no room for deviations in material performance or manufacturing error.
Properties and serviceability of 3D printed components are affected by their geometry, microstructure and defects. These important attributes are currently optimised by trial and error because the essential process variables can’t currently be selected from scientific principles.
A solution is to build and validate a Digital Twin of the 3D printing process capable of predicting of the spatial and temporal variations of metallurgical parameters affecting the structure and properties of components.
In principal, the Digital Twin of 3D printing process , when validated with accurate with experimental data would replace or reduce expensive, time consuming physical experiments with rapid inexpensive numerical experiments. In the initial phase, the Digital Twin would consider all the important 3D print process variables as input and provide a transient 3D model
“Creating “Digital Twins” Work Space for 3D Print Materials”
To ensure digital networking of production systems and the optimisation of material-specific requirements, we need to measure, assess and replicate the changes in material properties in a process where "Digital Twins" of materials are created.
The materials digital space has laid the groundwork for this process. When a finished part rolls off the production line, this is one of the first questions always asked: "Does this component have the properties we want?"
Often, even the tiniest of variations in the production environment are enough to alter a part’s material properties – and throw its functionality into question.
Manufacturers avoid this by close inspection of samples throughout the production process. Breaking down the samples into their composite parts and measuring them separately is an extremely time-consuming process.
"The outcome of the sample testing process branches out into an array of different subsets, each with their own specific measurement results. While experts may be able to keep an overview of the complex interrelationships in their heads, until now there has been no way to take the diversity of resulting data and portray it in a coherent digital format."
Now, for the first time, a proof of concept has been developed demonstrating that it is possible to digitally represent many such material processing cycles with a materials data space for test specimens produced using additive manufacturing.
"The data space concept allows us to integrate any type of material information into a digital network – a really valuable tool. We want to use the materials data space to automatically generate a digital twin of each material that will mirror the current state of the physical object under examination."
Data spaces can be used to integrate all types of materials information into digital networks. The advantage of the materials data space is that it provides an overview of all relevant parameters at a glance, whereas formerly data on different material parameters was scattered among numerous data repositories in many different formats.
But the real promise lies in the future. "In the years to come, the materials data space has the potential to become the production command center. Whenever component quality isn’t up to the expected standard, you can compare it with information on previous components stored in the materials data space to determine whether the present component can in fact be used or whether it must be rejected.
In the future, these results could be automatically integrated into industrial decision-making processes: whenever component quality dips below the required standard, production automatically comes to a halt.
Creating the data space –and managing the diversity of materials data – calls for a corresponding information model. "In this case, the model reflects the natural material world, in which material states and properties are assigned to defined categories.
The best way of thinking about it is in terms of a social network where each user is a node in the network. And in turn, these nodes have their own subject matter associations. What we do is to create semantic relationships between the individual material objects and their associated processing steps.
Then there are also interrelationships among these communities. What would be a “follow” on social media is represented in the materials data space by details on the chronological sequence of production or work steps, for instance "leaving the additive manufacturing process" or "this laser is part of the 3D printing process".
The new demonstrator for additively manufactured metal components has the capacity to generate samples, characterize the materials they contain, conduct subsequent data analysis and determine material properties. Thanks to the logic underpinning the model, users can make extremely complex queries of the data space that simply wouldn’t be possible with the same degree of flexibility in the case of a conventional database.
“Demonstration of Industrial 3D Printing used in Series Production with Automated Process Chain in Combination with Digital Twin”
Digital Twins are learning digital models of physical assets, parts, processes and even systems. The purpose of the Digital Twins is to relay data about the performance and properties of a physical counterpart. With this information, Digital Twins will achieve complete repeatability of a 3D printed part, and greatly improve process reliability.
The entire production process runs itself, without operating personnel, from a central, 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 the 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 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 significantly 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 3D print 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.
When it comes to replacement parts, 3D brings the advantage, going forward, of saving warehousing costs – because parts can instead be produced ‘on demand with the ‘Digital Twin network’, in other words the centralised availability of digital manufacturing data to allow the decentralised production of replacement parts using 3D printing.
1. Requirements should be mapped digital products/definition and requirements using top-down and validated bottom-up for conformity
2. Model-based thinking: Helps simplify the complexity of a multi-disciplinary product and its system of systems
3. Shift to maximising confguration management in the customer domain representing the set of connected systems and their structure, behaviour/requirements
4. Enhance effectiveness of product development by increasing responsiveness and ability to accommodate multiple refreshes in the product to meet changing customer demands.
5. Secure the pipeline of data and builds the configuration thread to manage info across the lifecycle of product components in conjunction with the digital twin.
6. Analytics and insights: Converts the abundant data available across the digital thread into meaningful information to provide insights for smart run time updates in product configuration.
7. Identify Configuration Item processes and develop method to uniquely identify each individual item
8. Create configuration control activity of managing project deliverables and related verification throughout the lifecycle of the product
9. Execute configuration Status verification to involve recording and reporting of all the changes status to the configuration items
10. Verify configuration of product and its components in order to ensure conformance to requirements by verifying the correctness of Status account information.