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Q&A The Knowledge of experts will not be obsolete in Industry 4.0

| Author / Editor: Nikolaus Fecht / Rosemarie Stahl

The term industry 4.0 is hyped especially at trade fairs, but the digital transformation is also met with fear among manufacturers and their professionals: will their expertise become obsolete in the age of digitalisation? Alexander Epple and Michael Königs explain, why the knowledge and experience of experts will still be needed in the future.

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Senior Engineer Alexander Epple from the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University: “In the aerospace industry, we’ve succeeded in raising productivity by almost 30 per cent, and at one German manufacturer of large machines by nearly 150 per cent.”
Senior Engineer Alexander Epple from the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University: “In the aerospace industry, we’ve succeeded in raising productivity by almost 30 per cent, and at one German manufacturer of large machines by nearly 150 per cent.”
(Source: WZL )

Some production experts will be looking at the EMO Hannover with mixed feelings and its “Industry 4.0 area”: they fear that Industry 4.0 will lead to algorithms and solutions themed around big data, which in the long term will render the experts’ knowledge superfluous. But that risk is dismissed by Senior Engineer Alexander Epple and Michael Königs from the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, who emphasise the role played by the interaction of big data and specialised expertise.

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Alexander Epple, as a Senior Engineer at the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, you head the Machine Data Analytics and NC Technology Department: how did this come about? Wouldn’t the post have been more suitable for a mathematician?

Alexander Epple: I admire mathematicians for their powerful algorithms and their capacity for tackling problems with a high degree of abstraction. These abilities also help when it comes to analysing big data. In the production world, due to the multiplicity of machines and processes involved, there are highly disparate kinds of data. Machines with the same processes permitting mutual comparisons are thus quite rare. Under these preconditions, purely statistical approaches are not very fruitful, and abstract big data approaches quickly come up against their limits in a production environment. It’s more fruitful to link knowledge of production technology, in the form of models, for instance, to the data concerned. This is why engineers have a place in the big data world as well.

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What is Big Data?

One way to define what constitutes big data is using the “three V's”: Volume, variety and velocity. This and many other definitions compare big data to smaller amounts of data that are still processable using traditional data processing applications. Another criteria is often seen in the addition of external data to processing internal data. In industry application this internal data is collected with sensors that measure all movements and conditions in the machine and parameters that affect the machine.

Moreover, big data sets are predominately hard to visualise. Another problem that arises is the issue of storage and processing. Today, a common solution is to store big data in the cloud. In addition to the management problem, the amount of collected data is constantly increasing while analysing methods and programs still are not broadly present in companies.

The hope that lies in big data is that by using complex algorithms and analysing methods manufacturers will get valuable insights that can be used for a more efficient and predictable production.

Does your team reflect this interdisciplinary approach?

Alexander Epple: We have six academics working in my team, who are supported by highly qualified programmers and machinery technicians. The team is in fact very interdisciplinary: we have not only mechanical engineers, but also computer scientists and electrical engineers. What’s more, I work very closely together with Dr. Marcel Fey and his Machine Technology Department, since his people possess extensive knowledge of modelling. Together, we harness the capabilities of almost 30 academics, which enables us to drive ideas effectively forward. At the moment, however, we’re still seeking to expand our team.

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