Interpretation Framework of Predictive Quality Models for Process- and Product-oriented Decision Support

Procedia CIRP, vol. 118, pp. 1066–1071

Abstract

In the context of predictive quality in production, there is a need to explain and understand the predictive models used, as well as the dependencies present in the underlying data. For this purpose, we develop a framework for model-independent interpretation of predictive models to enable data-driven, process- and product-oriented decisions. This framework combines different model-agnostic interpretation methods and structures them into two modules, one for a process-oriented view and the other for a product-oriented view. In addition, an implementation concept for the two modules in a machine learning pipeline is also provided.

Authors 4

1. Daniel Buschmann corresponding Aachen Laboratory forMachine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory forMachine Tools and Production Engineering (WZL), RWTH Aachen University, 52074 Aachen, Germany

2. Tobias Schulze Aachen Laboratory forMachine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory forMachine Tools and Production Engineering (WZL), RWTH Aachen University, 52074 Aachen, Germany

3. Chrismarie Enslin Aachen Information Management in Mechanical Engineering (IMA)

   RWTH Aachen University

   Affiliation as printed

   Information Management in Mechanical Engineering(IMA), RWTH Aachen University, 52068 Aachen, Germany

4. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology Laboratory forMachine Tools and Production Engineering (WZL)

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology (IPT), 52074 Aachen, Germany

   Laboratory forMachine Tools and Production Engineering (WZL), RWTH Aachen University, 52074 Aachen, Germany