Evaluation of process stability in precise electrochemical machining using machine learning models based on extracted features

Procedia CIRP, vol. 126, pp. 498–503

Abstract

The process advantages of precise electrochemical machining are confronted with demanding technology development due to the absence of adaptive process control. Consequently, sporadic process instabilities occur even in series production. However, it is known that process signals can provide valuable information about the working gap conditions necessary to predict such instabilities. Therefore, this paper presents the developments of a data-driven model based on process signals for evaluating machining stability. For this, process data are generated via experimental trials and suitable predictors capable of monitoring process stability are determined using feature extraction. Three machine learning models are then trained and tested.

Authors 6

1. Elio Tchoupe 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, Aachen 52074, Germany

2. Lukas Heidemanns 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, Aachen 52074, Germany

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

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

4. A. Klink 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, Aachen 52074, Germany

5. Tim Herrig 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, Aachen 52074, Germany

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

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