Digital twinning as a solution to manufacturing black boxes

Digital Twinning cobots with metaphorical black box

The ever complex manufacturing environment. A metaphorical black box.

While providing enormous benefits and opportunities for competitive advantage, the growth of sensor embedded systems and automation has made the modern manufacturing production environment increasingly complex. The growth rate of this complexity is not going to slow down anytime soon and is in fact growing exponentially.

In contrast to times gone by, this new normal of complexity is making it almost impossible for individuals to maintain an adequate overview of a complete production environment, especially in more advanced industries. Where before individual staff were able to understand the complexities of the manufacturing floor and its machines, this is no longer the case. Let alone, understand the impact of changes within the processes or production environment.

An increasingly complex production system turns into a metaphorical black box. A black box where the general functionality and process is understood, but the internal workings of systems, machines and processes are seldom understood in their entirety by one technician or operator. These black boxes make it hard to oversee the complete production environment for the employees and the company itself. Especially when looking through the lens at different control levels in the production environments and different perspectives, it can become increasingly difficult to capture a holistic overview of a system.

Digital Twins as a solution to complex data
Digital twinning could be the solution to the industrial need for more control and insight into our modern manufacturing ecosystems and infrastructure. Digital twinning can act as a bridge between the physical and virtual environment. It can be used to simulate changes in environments, processes, and individual machines, representing the data in its context and illustrating the impact of changes virtually. Therefore, using the increase of knowledge and application of sensor technology to its advantage. By putting the acquired data in its context, it will be more tangible and understandable, making it easier to relate and adjust the changes to the physical world.

Digital Twinning is expected to become essential with the rise of advanced technology and many industries are already exploring and implementing Digital Twinning principles. Modern energy infrastructure systems, the development of autonomous vehicle systems and applications in the aerospace are only a few examples of areas where we are seeing the application of this virtual-physical bridge.

What digital twinning can also offer us in a manufacturing setting is the missing link between captured data and purposeful application. This link can be used for formulating a strategy for action. Complex data can be represented in a meaningful contextualized way that is relevant to the user that needs the information. It reduces the avalanche of complex system data that is so often either meaningless or too unmanageable to work through to reach a practical solution. The data can be presented in the right perspective and at the right level of aggregation to provide context that is practical.

Problems and errors in complex systems can also be presented in a way that shows what is happening in the context of real-world datasets or processes. By doing this, what happens within the metaphorical black box will become tangible and understandable. Cross-checking this virtual representation with reality can allow for new insights that are formed by either identifying missing data points, or by finding a misrepresentation between virtual and physical entities. The primary benefit we see here is better-informed decision making and providing a flexible digital learning environment that supports and guides in decision making, safely finetuning and testing different scenarios without risking damage or downtime of valuable assets.

In summary, a digital twin system will allow for the complexity of the system to be reduced and the mystery of the black boxes contents unpacked and understood. Maintaining a holistic overview will become easier, giving insight back to the company and its employees. This will allow for more informed and better decisions.

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Maaike Slot


Maaike Slot is a passionate research engineer specialising in digitalisation within the manufacturing industry. Working at the FIP-AM@UT since 2019, Maaike has been involved in several projects focused on optimising production processes through digitalisation and innovation. Simultaneously, Maaike is pursuing a Ph.D. with a specific emphasis on Digital Twinning in production environments.