Self-optimizing process planning of multi-step polishing processes

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.