Induction hardening is a heat treatment that has been increasingly employed in the industry in recent years. It is a complex, highly coupled, and multiphysical process involving electromagnetism, thermal, mechanical, and metallurgical physics. One of the main quality requirements of the process is the hardened case depth generated in the workpiece. The usual method to measure the hardened case and ensure the quality of the parts is to use destructive techniques, which generate material and energy waste and production inefficiencies. Additionally, selecting process parameters such as current, frequency, or scanning speed typically requires several trial-and-error iterations. The goal of this work is to provide a hybrid digital twin (DT) that acts as a nondestructive test technique, predicting the resulting hardened case in real-time and enabling the correction of process parameters during the induction hardening process, ultimately achieving a zero-waste manufacturing scheme. For this purpose, a DT based on an artificial neural network (ANN) model is developed, predicting the hardened case depth in real-time using four monitored input variables: induction frequency, current, and two temperature measurements on the surface of the hardened part. The required data for DT development and training are obtained using a finite element model. Several ANN architectures are evaluated, and the configuration with the best regression results is chosen for implementation in an industrial induction hardening machine. The hardened case predictions obtained from the developed DT demonstrate high accuracy within the analyzed frequency and current range.
López-Lombardero et al. (2025) studied this question.