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