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.

Analyzing the chip formation process by means of the finite element method (FEM) is an established procedure to understand the cutting process. For a realistic simulation, different input models are required, among which the material model is crucial. To determine the underlying material model parameters, inverse methods have found an increasing acceptance within the last decade. The calculated model parameters exhibit good validity within the domain of investigation, but suffer from their non-uniqueness. To overcome the drawback of the non-uniqueness, the literature suggests either to enlarge the domain of experimental investigations or to use more process observables as validation parameters. This paper presents a novel approach merging both suggestions: a fully automatized procedure in conjunction with the use of multiple process observables is utilized to investigate the non-uniqueness of material model parameters for the domain of cutting simulations. The underlying approach is two-fold: Firstly, the accuracy of the evaluated process observables from FE simulations is enhanced by establishing an automatized routine. Secondly, the number of process observables that are considered in the inverse approach is increased. For this purpose, the cutting force, cutting normal force, chip temperature, chip thickness, and chip radius are taken into account. It was shown that multiple parameter sets of the material model can result in almost identical simulation results in terms of the simulated process observables and the local material loads.

The tribological effect of cutting fluids in the machining processes to reduce the friction in the cutting zone is still widely unknown. Most test benches and procedures do not represent the contact conditions of machining processes adequately, especially for interrupted contacts. This results in a lack of knowledge of the tribological behavior in machining processes. To close this knowledge gap, a novel experimental test bench to investigate the effects of cutting fluids on the frictional conditions in metal cutting under high-pressure cutting fluid supply was developed and utilized within this work. The results show that there is a difference between the frictional forces in interrupted contact compared to continuous contact. Furthermore, the cutting fluid parameters of supply pressure, volumetric flow rate, and impact point of the cutting fluid jet influence the frictional forces, the intensities of which depend on the workpiece material. In conclusion, the novel test bench allows examining the frictional behavior in interrupted cuts with an unprecedented precision, which contributes to a knowledge-based design of the cutting fluid supply for cutting tools.

Numerical modeling of machining processes exhibits a high potential for shortening process development times. When modeling the machining process, an accurate material model is essential for the success and reliability of the simulated results. Especially, the simulation results depend largely on...