EXHIBITIONS
Engineers have a place in the big data world too
EMO Hannover 2017 shows specific solutional approaches for dealing with big data
t’s with mixed feelings that some production experts will be looking at the EMO Hannover 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
I
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.
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. Does your team reflect this interdisciplinary approach?
30 IMT June 2017
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.
How widespread is this cross- disciplinary approach in practice? Michael Königs: In the field of simulation, particularly, we’ve had interdisciplinary teams at the WZL for a long time already. This approach has likewise proved its worth at other universities or research institutions. But we are also observing that interdisciplinary teams are no longer merely optional in the context of model-based near-real-time data processing; on the contrary, in future there will be no alternative to this sort of collaboration. Linking and bringing together methods and models from different specialisms is essential for unlocking the vast potentials involved in data analyses. So to sum up, it can be said that there have always been interdisciplinary approaches, but in future these will gain even further in importance.
Michel Königs, you are one of the computer scientists: how do you approach the world of mechanical engineering? Michael Königs: When it comes to practical applications, you very quickly learn that the data quality of the signals
Alexander Epple
recorded is crucial to the success of an analysis. Contrary to what a lot of people think, the data don’t always contain everything you need. For example, metrological systems in a machine tool supply position data, yes, but these provide only an approximation of the tool’s real path during a milling operation. There’s usually no way to draw conclusions on deflection effects, for instance, as a result of process forces or geometrical-kinematic inaccuracies of the machine tool being used. Knowledge of modelling can be employed to enrich the pure signal data with this missing information. This refined data record is essential, you see, for predicting the workpiece qualities being achieved during the actual machining process.
How to deal with the gigantic quantities of data involved? Industry 4.0 leads to transparent production facilities, which thanks to the increase in the sensor technology fitted and their powerful evaluation electronics will generate big data. But how can the valuable raw data of a machine tool, for example, be acquired – can a relatively old machine without any sensor technology be retrofitted with it? Alexander Epple: There are research projects that examine how relatively old machines can be retrofitted with the requisite sensors. In addition, we are currently
www.internationalmetaltube.com
pursuing approaches that initially utilise the sensor technology installed in the machine. Besides the motor current, each machine also acquires ongoing axis positions. Direct and indirect path measuring systems are usually installed. We can use these signals, for example, for process-concurrent determination of the propensity to vibrations. The process forces and component loadings can also be determined for approaches in terms of predictive maintenance. This is true for both old and for new machinery systems.
But big isn’t always beautiful: 100-per-cent data acquisition from the work of a machining centre, for instance, already means in realtime (35 process parameters per millisecond) an annual data volume of 5.8 terabytes. How do you filter out the interesting facts from it? Michael Königs:
For extracting interesting information and process
parameters, we use both statistical methods (machine learning) and algorithms developed specifically for this purpose, which enable expert and domain knowledge to be integrated. Quite generally, though, it’s correct to say that continuous data acquisition will entail a huge quantity of data. There are approaches now that involve not acquiring all data continuously at the maximum scanning rate,
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36
