Reinforcement learning supported quality control loop for solid forming processes

Procedia CIRP, vol. 138, pp. 709–714

Abstract

In solid forming, the ramp-up phase of new batches poses a challenge due to process instabilities and frequent dependence on expert knowledge. Reinforcement Learning can be used to mitigate process instabilities and lead to a quicker fulfillment of the required quality characteristics. A quality control loop (QCL) based on reinforcement learning (RL) has been developed to determine the optimal process parameters for solid forming processes. The QCL continually provides recommendations for process parameters based on quality characteristics. The validation and benchmarking of the QCL is carried out based on a use case in the solid forming industry.

Authors 4

1. Ronja Witt corresponding Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

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

2. Chris R. Schönekehs Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

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

3. Nils Klasen Aachen Laboratory for Machine Tools and Production Engineering WZL

   RWTH Aachen University

   Affiliation as printed

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

4. 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, Steinbachstraße 17, 52074 Aachen, Germany

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