Machine Learning Applications for Thermal Manufacturing Processes
Peter Weiderer · University of Regensburg Publication Server (University of Regensburg) · 2019
This thesis introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. I treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. The proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. It is shown how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. The proposed methods are applied to real world data, collected in a foundry during the series production of casting parts for the automobile industry.