Identification of the Parameter Values of the Constitutive and Friction Models in Machining Using EGO Algorithm: Application to Ti6Al4V

Abstract

The application of artificial intelligence and increasing high-speed computational performance is still not fully explored in the field of numerical modeling and simulation of machining processes. The efficiency of the numerical model to predict the observables depends on various inputs. The most important and challenging inputs are the material behavior of the work material and the friction conditions during the cutting operation. The parameters of the material model and the friction model have a decisive impact on the simulated results. To reduce the expensive experimentation cost that gives limited data for the parameters, an inverse methodology to identify the parameter values of those inputs is suggested to potentially have data of better quality. This paper introduces a novel approach for the inverse identification of model parameters by implementing the Efficient Global Optimization algorithm. In this work, a method relying on a complete automated Finite Element simulation-based optimization algorithm is implemented to inversely identify the value of the Johnson–Cook (JC) parameters and Coulomb’s friction coefficient correlatively, where the objective function is defined as minimizing the error difference between experimental and numerical results. The Ti6Al4V Grade 5 alloy material is considered as a work material, and the identified parameters sets are validated by comparing the simulated results with experimental results. The developed automation process reduces the computation time and eliminating human errors. The identified model parameters value predicts the cutting force as 169 N/mm (2% deviation from experiments), feed force as 55 N/mm (7% deviation from experiments), and chip thickness as 0.150 mm (11% deviation from experiments). Overall, the identified model parameters set improves the prediction accuracy of the finite element model by 32% compared with the best-identified parameters set in the literature.

A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines

“…The Downhill Simplex algorithm was applied to data collected from cutting tests for AISI 1045 to determine the J-C material model parameters that delivered simulation results in good agreement with the process observables such as temperature, cutting force, and chip form [22,23]. The Efficient Global Optimization algorithm (EGO) was applied to correlatively determine the J-C parameters as well as the Coulomb's friction coefficients by minimizing the error between the numerical and experimental results for Ti6Al4V Grade 5 alloy [24].…”

Multi-Response Optimization and Influence of Expanded Graphite on Performance of WEDM Process of Ti6Al4V

“…Ti6Al4V has a low mass ratio, superior bio-absorbability, good anti-corrosive properties, high endurance, and increased fracture resilience [4,5]. While the aerospace sector still prevails in the Ti6Al4V market, other application areas like the automotive, energy, maritime, and chemical-related sectors, load-bearing implants, fixing bone deformities, and other biomedical sectors have seen significant growth over the past 50 years [6][7][8][9]. However, Ti6Al4V poses many challenges during machining due to its high chemical reactivity with cutting tool materials and limited heat conductivity [10,11].…”