WS-B2: Production Technology – Predictive Maintenance

 

The Internet of Production (IoP) is based on the vision to enable a new level of cross-domain collaboration by providing semantically adequate and contextual data from production, development and usage in real time and adapted granularity. The central scientific approach is to provide digital shadows as application-specific aggregated and multi-perspective data sets. In the Cluster of Excellence, a conceptual reference infrastructure will be designed and implemented to enable the generation and use of digital shadows.

The production is characterized by a broad spectrum of highly specialized technologies. These include technologies such as casting, forming, metal cutting, injection moulding, extrusion, weaving, welding, rolling, electroerosion, physical vapour deposition, electrochemical processing and laser material deposition as well as new technologies such as selective laser melting. There is extensive expert knowledge for the individual production technologies, but details and interactions are not yet fully understood in complex manufacturing contexts. The main research objective of CRD-B2 is the integration of reduced and heterogeneous engineering models across production areas into situation-specific real-time data analysis.

The prediction of wear and fatigue of machine components is one of the most challenging tasks in production engineering. This is due to the fact that stochastic causes such as adhesion, abrasion, erosion or corrosion can hardly be measured directly or parallel to the process.
The workstream B2 of the Department of Machine Tools concentrates on the combination and evaluation of condition and load correlating signals such as drive currents and accelerations, thus enabling a component failure to be predicted. For this purpose, a demonstrator is set up in the production environment.