Quality prediction for milling processes: automated parametrization of an end-to-end machine learning pipeline

Abstract

The application of modern edge computing solutions within machine tools increasingly empowers the recording and further processing of internal data streams. The datasets derived by contextualized data acquisition form the basis for the development of novel data-driven approaches for quality monitoring. Nevertheless, for the desired data-driven modeling and data handling, heavily specialized human resources are required. Additionally, domain experts are indispensable for adequate data preparation. To reduce the manual effort regarding data analysis and modeling this paper presents a new approach for an automated parametrization of an end-to-end machine learning pipeline (MLPL) to develop and select the best-performing quality prediction models for usage in machining production. This supports domain experts with a lack of specific knowledge of data science to develop well-performing models for machine learning-based quality prediction of milled workpieces. The results show that the presented algorithm enables the automated generation of data-driven models at high prediction performances to use for quality monitoring systems. The algorithm’s performance is tested and evaluated on four real-world datasets to ensure transferability.