Applied Machine Learning
In industrial machining processes, the wear of a tool has a significant influence on the quality of the produced part. Therefore, predicting wear upfront can result in significant improvements of machining processes. In this work, the applicability of machine learning approaches for predicting tool wear in industrial milling processes was investigated, based on real-world sensor data on exerted cutting forces, acoustic emission and acceleration.
As part of Sirris’ EluciDATA Lab, Mathias Verbeke focusses on supporting companies in creating new or improved products and services based on targeted data exploitation using data science and machine learning. He contributes to, coordinates and sets up (inter)national, industry-driven R&D projects on different industrial topics, including fleet-based analytics, product usage monitoring, digital servitisation, and data-driven optimisation of machining processes.