Hybrid ML for Parameter Prediction in Production

Procedia CIRP, vol. 118, pp. 822–827

Abstract

In the past, research in the production domain was driven by mathematical and physical description of production technologies. Over the last years, data-driven approaches like machine learning (ML) and artificial intelligence (AI) gave the research a new direction. Often, already existing knowledge is neglected when using data-driven approaches resulting in models that do not represent the best possible results. By combining these two approaches all available knowledge is used generating the best possible model. This combination is called hybrid modeling. In this paper, hybrid ML as part of hybrid modeling is introduced and the benefits and challenges using hybrid ML for the prediction of process parameters in the production domain are demonstrated.

Authors 3

1. Jonas Dorißen Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

2. Henrik Heymann corresponding Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

3. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University · Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

   Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University, Campus-Boulevard 30, Aachen 52074, Germany