Considering multiple process observables to determine material model parameters for FE-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.

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.

A study on the influence of pick geometry on rock cutting based on full-scale cutting test and simulation

In this paper, series of full-scale cutting tests and cutting simulations are carried out to investigate the influence of installation parameter and geometry of the pick on cutting performance. The discrete element method is used to simulate the rock cutting process. A general process to calibrate macro properties of rock including uniaxial compressive strength (UCS), elastic modulus, Poisson’s ratio, cohesion and internal friction angle is proposed and used to complete the calibration of coal model. The cutting simulations are performed using picks with different tip angles and rake angles. The results show that the peak cutting force (PCF) decreases with the increase of rake angle following an inverse proportional function when the rake angle is positive, while it varies following a parabolic curve in the condition of negative rake angle. Moreover, the crack mode changes from primarily shear failure to primarily tensile failure with the increase of rake angle. Finally, a multiple-attribute index is proposed to evaluate the cutting performance and select the optimum cutting parameters.