Data-Driven Inverse Problem for Optimizing the Induction Hardening Process of C45 Spur-Gear

Abstract

Inverse problems can be challenging and interesting to study in the context of metallurgical processes. This work aims to carry out a method for inverse modeling for simultaneous double-frequency induction hardening process. In this investigation, the experimental measured hardness profiles were considered as input data, while the output data were the process parameters. For this purpose experiments were carried out on C45 steel spur-gear. The method is based on machine learning algorithms and data treatment for dealing with inverse approach issues. In addition to the inverse modeling, a forward problem-based verification completes the study. It was found that according to promising results that this method is suitable and applicable for inverse problem of hardness modeling.