Automated machine learning for predictive quality in production

Procedia CIRP, vol. 93, pp. 443–448

Abstract

Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan.

Authors 4

1. Jonathan Krauß 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

2. Bruno Machado Pacheco 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. Hanno Maximilian Zang 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

4. Robert Heinrich Schmitt Aachen Laboratory for Machine Tools WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools WZL RWTH Aachen University, Steinbachstr. 19, Aachen 52074, Germany