On the Application of the Particle Swarm Optimization to the Inverse Determination of Material Model Parameters for Cutting Simulations

The manufacturing industry is confronted with increasing demands for digitalization. To realize a digital twin of the cutting process, an increase of the model reliability of the virtual representation becomes necessary. Thereby, different models are required to represent the experimental behavior of the workpiece material or frictional interactions. One of the most utilized material models is the Johnson–Cook material model. The material model parameters are determined either by conventional or by non-conventional material tests, or inversely from the cutting process. However, the inverse parameter determination, where the model parameters are iteratively modified until a sufficient agreement between experimental and numerical results is reached, is not robust and requires a high number of iterations. In this paper, an approach for the inverse determination of material model parameters based on the Particle Swarm Optimization (PSO) is presented. The approach was investigated by the inverse re-identification of an initial parameter set. The conducted investigations showed that a material model parameter set can be determined within a small number of iterations. Thereby, the determined material model parameters resulted in deviations of approximately 1% in comparison to their target values. It was shown that the PSO is suitable for the inverse material parameter determination from cutting simulations.