Self-optimizing process planning of multi-step polishing processes

verfasst von

Berend Denkena, Marc André Dittrich, Hai Nam Nguyen, Konrad Bild

Abstract

Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.

Organisationseinheit(en)

Institut für Fertigungstechnik und Werkzeugmaschinen
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme

Typ

Artikel

Journal

Production Engineering

Band

15

Seiten

563-571

Anzahl der Seiten

9

ISSN

0944-6524

Publikationsdatum

06.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Maschinenbau, Wirtschaftsingenieurwesen und Fertigungstechnik

Elektronische Version(en)

https://doi.org/10.1007/s11740-021-01042-6 (Zugang: Offen)