Enhanced frequency adaptation approaches for series resonant inverter control under workpiece permeability effect for induction hardening applications

Abstract

Abstract Induction Hardening (IH) process contributes to the improvement of the mechanical properties of the steel. In this application, the inverter operates at resonant frequency for high operating performances. However, this is usually accompanied by steel characteristics variation under temperature effect especially the magnetic permeability. This paper proposes two approaches for resonance frequency adaptation. The first is based on maximum efficiency tracking (MET) scheme. In this context, simulation with an algorithm for control is developed to find in real-time the adequate frequency based on current and voltage sensor. This approach is found effective but requires a constant determination of the input and the output power. Hence, it makes it difficult for implementation. To alleviate this drawback, advanced technique based on deep learning (DL) algorithm is proposed. Thus, an experimental prototype is built to determine the experimental Temperature-Frequency data profiles up to the hardening for two metal bars. Hence, a neural network (NN) based model control is elaborated. After a careful selection of NN parameters, a trained model is obtained with satisfactory accuracy leading to the predict model. This was easily implemented in real time on raspberry pi 3\xa0+\xa0and allows the system to perform continuously under high efficiency for hardening purposes.