Development of a Multi-physical Kinetics Model for Electrodynamic Machine Hammer Peening Using Machine Learning Approaches

Abstract

Machine hammer peening (MHP) is a cold forming process for incremental finishing of metallic surface layers. The impact energy of the electrodynamic MHP results from conversion of electrical energy into mechanical energy and finally from energy transfer to the workpiece. The interactions between the impact energy and the material properties determine the resulting surface layer properties. Thus, the knowledge of the kinetic impact energy is crucial for defined process control. Nonetheless, the kinetic impact energy is difficult to determine due to complex influences of electrical and mechanical parameters during energy conversion. Therefore, the objective of this work is to identify the correlations between the impact energy resulting from electrical and mechanical influences. For this purpose, an electrodynamic MHP system was characterized experimentally and a databased multi-physical kinetics model was developed. By using machine-learning methods, the model enables the prediction and targeted design of impact energies depending on MHP process parameters.