Application of genetic algorithms for optimizing the Johnson–Cook constitutive model parameters when simulating the titanium alloy Ti-6Al-4V machining process

Abstract

Finite element simulation of metal machining requires accurate constitutive models to characterize the material stress–strain response in plastic deformation processes. An optimization methodology using genetic algorithms was developed to determine the Johnson–Cook material model for Ti–6Al–4V alloy. The optimization of the parameters resulted in lower errors between the calculated flow stress and the experimental values obtained through the split Hopkinson pressure bar tests at different temperatures (ranging from 25 °C to 900 °C) and strain rates (2000 and 2500s−1). Optimized Johnson–Cook constitutive parameters were used to calculate the flow stress under various conditions. The calculated results showed excellent agreement with the experimental values, with errors lower than 4%. In addition, Ti–6Al–4V alloy orthogonal cutting experiments were carried out to validate the finite element simulation results. The experimental chip morphology was compared with the simulation results obtained by the optimized Johnson–Cook model (M2) and the original Johnson–Cook model (M1). The simulated results (including chip morphology and cutting force) were affected by the flow stress model. Comparison of the experimental and simulated results revealed that the optimized Johnson–Cook model can provide relatively good prediction results for the titanium alloy machining process, especially for chip morphology prediction.