Towards adaptive electrochemical machining via signal-based data-driven modelling

CIRP Annals, vol. 75, pp. 221–225

Abstract

Electrochemical machining remains largely restricted to medium- and large-batch production and to companies with substantial prior experience despite its significant potential, driven by advantages such as contact-free machining and virtually wear-free operation. This limitation arises from the strongly experience-based nature of present process setups. Reliable process monitoring is a prerequisite for the implementation of adaptive and closed-loop control. This paper demonstrates how process dynamics can be monitored using data-driven models trained on historical machining signals, enabling the inference of prevailing process conditions directly from measured data and forming a basis for future closed-loop process control.

Authors 3

1. Elio Tchoupe Sambou corresponding Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute, MTI of RWTH Aachen University, Aachen, Germany

2. A. Klink Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute, MTI of RWTH Aachen University, Aachen, Germany

3. Tim Herrig Aachen Manufacturing Technology Institute (MTI)

   RWTH Aachen University

   Affiliation as printed

   Manufacturing Technology Institute, MTI of RWTH Aachen University, Aachen, Germany