The Knowledge of experts will not be obsolete in Industry 4.0
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 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.
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 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.