Digital Twins: a Catalyst for Developing Environmentally Sustainable Manufacturing – Alessio Fino
YML Contest for the 2021 WM Report
From March to July 2021, the YML Contest for the 2021 World Manufacturing Report was held, inviting young leaders from all over the world to submit a case study relevant to the topic of Digitally Enabled Circular Manufacturing. The submissions were evaluated by the World Manufacturing Foundation and the winning case studies are included in the publication.
Digital Twins: a Catalyst for Developing Environmentally Sustainable Manufacturing
Alessio Fino Bachelor’s Degree Student, Politecnico di Torino – YML Milan City Hub
A Digital Twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It not only showcases form and materials, but also analyses the function of the object and the way it works in a real-life context.1 This is achieved by combining experimental data and mathematical models, in particular finite element analysis (FEA).
Finite Element Analysis (FEA)2 is the simulation of a physical phenomenon using a numerical mathematic technique referred to as the Finite Element Method (FEM). This process is at the core of mechanical engineering, as well as a variety of other disciplines. Its current applications are fundamental for the understanding of the mechanical behaviour of materials as well as more complex structures. In the same way as this can help researchers to understand the nature of a component or a material, it can teach the software how to run the simulation of the digital twin.
Products, as well as whole machines and factories, can be represented with a digital twin, which is able to simulate the structure and the activity of the real-world counterpart, also acquiring real-time data from it so than it can be compared to the digital simulation of the twin, in order to eliminate inefficiencies and correcting faulty processes. For this reason, they are the backbone of Industry 4.0, and their application can contribute enormously to the sustainability of the manufacturing processes. The continuous comparison between real-time data and data obtained from the simulation can be used by the software to improve the digital twin’s understanding of the realworld issue, hence resulting in more accurate simulations.
This case study highlights two ways through which digital twins can help to improve the sustainability of production processes. The first is by comparing real-world data to the digital simulation of manufacturing systems in order to find flaws and inefficiencies. By comparing the simulation with data from sensors, it is possible to isolate the location and even the cause of flaws in the system with absolute precision. This very process can be replicated using a digital twin of the supply chain that runs behind every industrial activity and can help to point out inefficiencies and bottlenecks throughout the chain. The improvements in efficiency achievable by optimising the manufacturing system can greatly reduce the environmental impact, also leading to a smarter use of resources.
On the other hand, a digital twin can also be used to implement new secondary functions, such as finding new ways to recycle and reuse waste products resulting from manufacturing processes by running data regarding the nature of the waste products through Artificial Intelligence (AI) generative algorithms. This would enable the simulation of possible applications of waste products so that they can be cleverly recycled or reused.
This is a crucial point when it comes to designing out waste from the manufacturing processes, a further step forward in the transition to a circular economy.
A clever application of digital twins coupled with generative algorithms was made by Siemens in 2019 while designing a gas mixing system: using simulations of Forman flow behaviour, AI was able to design a unique channel shape and configuration, which was not only significantly more efficient than previous designs, but was also designed in a relatively short period of time. Its related digital twin was also able to run tests and reliably simulate the behaviour of the product before it was even manufactured.
This process can be scaled to the size of factories and, coupled with technologies like gas atomisation and 3D printing using additive manufacturing, it can create production systems with a degree of flexibility never seen before, which can potentially lead to having fewer production lines which can produce a wider variety of products, enormously reducing the carbon footprint of factories. Furthermore, as observed in the case of Siemens’ gas mixing system, digital twins would significantly speed up the experimental applications of new materials, processes and structures also with the help of FEA. Such applications have the potential to reduce the timespan from design to mass production by several orders of magnitude. A digital twin could, for example, run series of algorithms that cross reference composition and mechanical proprieties of existing alloys with FEA to simulate the proprieties of possible alloys that can be created with the waste metals of a manufacturing system, then using gas atomisation to create the actual alloy. The development of the new alloy would not only be quicker, but also simpler because the digital twin can take on time-consuming tasks such as running simulations.
In order to support such implementations, it is crucial that manufacturing systems become leaner, more resilient and have a high degree of reconfigurability; without a substantial improvement in these characteristics, none of the abovementioned benefits will be achieved. The reasoning is simple: having more resilient manufacturing systems allows the production of a wider variety of products using the same infrastructure, hence fewer manufacturing plants, hence fewer power plants to keep the factories going while maintaining the same production output. Digital twins can also help in this field by running simulations of new concepts of manufacturing systems to determine the degree of reconfigurability and the other abovementioned parameters, before the actual site is even built. Reconfigurable Manufacturing Systems (RMS) are designed in such a way as to sustain rapid cost-effective change in structure, as outlined by Dolgui and Proth3, “RMSs are designed to permit quick changes in the system configurations, their machines and controls in order to adjust to market changes”. This is so important because running an RMS in a factory would bring down the fixed costs for applying changes to the manufacturing system, thus making it easier to implement more sustainable solutions while also allowing the enactment of changes in a shorter timespan, forcing the system to remain idle for less time during upgrades.
These are just a few examples of what digital twins are capable of; their applications in the world of manufacturing are very diversified and still under development. As explored in the previous cases, this technology is bound to play a key role in the sustainability of manufacturing processes. It would improve resource efficiency by optimising existing processes and producing components that can retain their mechanical proprieties, while also using less material.
Given all the information above, digital twins have the characteristics to act as catalysers for a new, more sustainable industry, making innovation cheaper and easier to implement in every field of engineering.
1 El Saddik, A. (2018)Digital Twins: The Convergence of Multimedia Technologies, IEEE Multimedia, Volume 25.
2 Roylance, D. (2001). Finite element analysis. Department of Materials Science and Engineering, Massachusetts Institute of Technology.
3 Dolgui, A., Proth, J. M. (2010). Pricing strategy and models. Annual reviews in control, Volume 34.