An ML-based approach for inverse identification of heat flux in machining

Paper in proceeding, 2022

This study presents an efficient and robust inverse approach to obtain the heat flux distribution on the tool rake face in oblique cutting including the tool nose radius. In this approach, Machine Learning (ML) is used to establish the relation between the parameters associated with the heat flux distribution and the error functions expressing the deviation between the embedded thermocouple measurements and Finite Element (FE) simulations. The dependency of the algorithm on the number of input data, the optimization strategy, and the overall performance of the approach are studied. The results show a clear potential of the proposed ML-based inverse identification approach.