Transfer learning for enabling quality predictions in small batch production

Procedia CIRP, vol. 138, pp. 1049–1054

Abstract

Multilayer perceptrons (MLP) have found wide application in the prediction of quality features within production processes. This type of model requires many training data points for reasonable accuracy. Production processes with small batches pose a challenge for model training due to a high variance with a relatively small data quantity for individual product batches. The article shows possibilities for making cross-variant quality predictions through the application of transfer learning algorithms. A framework that allows companies to evaluate the feasibility of various transfer learning approaches for predictive quality applications within different product batches is developed and presented.

Authors 6

1. Nils Klasen 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. Ravish Kumbhare 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. Dominic Alex Joseph

   German University of Technology

   Affiliation as printed

   Department of Computer Science, German University of Technology in Oman, Athaibah PC 130, Muscat, Sultanate of Oman

4. 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

5. Ronja Witt 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

6. Robert H. Schmitt Aachen Fraunhofer Insititute for Production Technology IPT Laboratory for Machine Tools and Production Engineering (WZL)

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Insititute 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