Advancing data-driven process modeling in metal forming

at - Automatisierungstechnik, vol. 73, pp. 162–173

Abstract

Abstract The production of metal forming technology products until now requires substantial expertise from specialists in product, process, and equipment design. Particularly important is the ability to compensate for stochastic, unpredictable process deviations. Given this context, a newly established Priority Program 2422 (PP 2422) by the German Research Foundation (DFG) aims to enhance the current FEM-simulation-based design of the active surfaces of metal forming tools with data-driven modeling. The present paper firstly summarizes the current state of the art in data-based process modelling in this technology. Subsequently, the research questions addressed by the PP 2422 and the concept of interdisciplinary cooperation between metal forming, automation and data science are explained.

Authors 5

1. Mathias Liewald

   University of Stuttgart

   Affiliation as printed

   Institute for Metal Forming Technology , University of Stuttgart , Stuttgart , Germany

2. Birgit Vogel‐Heuser

   Technical University of Munich

   Affiliation as printed

   Institute of Automation and Information Systems (AIS), TUM School of Engineering and Design , Munich , Germany

3. Thomas Bergs Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute (MTI), RWTH Aachen University , Aachen , Germany

4. Marco F. Huber

   University of Stuttgart

   Affiliation as printed

   Institute of Industrial Manufacturing and Management IFF, University of Stuttgart , Stuttgart , Germany

5. Peer Kröger

   Christian-Albrechts-Universität zu Kiel

   Affiliation as printed

   Department of Computer Science, Christian-Albrechts-Universität zu Kiel , Kiel , Germany