Q&A

The Knowledge of experts will not be obsolete in Industry 4.0

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How did you come to be collaborating with the experts at SAP, who together with Cisco and Huawei have developed a big data client that acquires and stores all data in the cycle of the CNC?

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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.

Alexander Epple: The close collaboration was initiated by SAP, since they were looking for a research partner from the field of production technology, able to provide not only excellent basic R&D but also a lengthy track record in application-focused collaboration with industrial enterprises. We have supported SAP with customer-related projects in highly disparate fields. The results were a surprise even to us. For instance, at a German automaker, with SAP we have increased productivity by 30 per cent in the power train section, and substantially reduced the rejection rate. In the aerospace industry, we’ve likewise succeeded in raising productivity by almost 30 per cent, and at one German manufacturer of large machines by nearly 150 per cent.

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What are the typical questions you’re confronted with?

Alexander Epple: Machine operators, process developers or quality engineers are often worried that their expert knowledge will not be needed any more in the long term. However, we believe that all essential decisions will still have to be taken by experts. They are familiar with numerous boundary conditions that may not even be amenable to being imaged by means of data. Data evaluation has to support the operator in his/her decision-making with appropriate visualisation of machinery or process states. Thanks to the new solutions, however, operators will in particular be spared the elaborate search and pre-processing work involved for individual pieces of process information, with its minimal contribution towards added value.

The relevant workload in the metalworking sector is high: could virtual prototyping or try-out be sufficiently improved with the aid of big data so that the number of real trials could be reduced or even eliminated entirely?

Alexander Epple: Substantial reductions are indubitably possible, yes. In our view, learning from data with the support of models has huge potential.

What do you and your team expect from the EMO Hannover, the world’s premier trade fair for the metalworking sector. What role will big data play there?

Alexander Epple: Due to our collaboration with numerous industrial partners, who will also be represented at the EMO Hannover, we already have a pretty definite idea: I believe it’s becoming progressively clearer that the use of big data in production operations can be substantially enhanced by incorporating specialist expertise. This eliminates the worries expressed by many specialist employees that their expert knowledge will soon be rendered superfluous by big data. I am hoping for more acceptance from the visitors and a certain amount of curiosity from a sector that otherwise tends to be rather conservative. So I’m looking forward to plenty of specific solutional approaches.

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