An internal state variable plasticity-based approach to determine dynamic loading history effects on material property in manufacturing processes

Abstract

Worked materials in large deformation processes such as forming and machining experience a broad range of strain, strain rate, and temperatures, which in turn affect the flow stress. However, the flow stress also highly depends on many other factors such as strain path, strain rate and temperature history. Only a model that includes all of these pertinent factors is capable of predicting complex stress state in material deformation. In this paper, the commonly used phenomenological plasticity models (Johnson–Cook, Usui, etc.) to characterize material behavior in forming and machining were critically reviewed. Although these models are easy to apply and can describe the general response of material deformation, these models lack the mechanisms to reflect static and dynamic recovery and the effects of load path and strain rate history in large deformation processes. These effects are essential to understand process mechanisms, especially surface integrity (residual stress, microhardness, and microstructure) of the manufactured products. As such a dislocation-based internal state variable (ISV) plasticity model was used, in which the evolution equations enable the prediction of strain rate history and temperature history effects. These effects can be quite large and cannot be modeled by the equation-of-state models that assume that stress is a unique function of the total strain, strain rate, and temperature, independent of the loading path. The temperature dependence of the hardening and recovery functions results in the prediction of thermal softening during adiabatic temperatures rises, which are common in metal forming and machining. The dynamic mechanical behaviors of three different benchmark work materials, titanium Ti-6Al-4V, AISI 52100 steel (62 HRc), and aluminum 6061-T6, were modeled using the ISV approach. The material constants were obtained by using a nonlinear regression-fitting algorithm in which the stress–strain curves from the model were correlated to the experiments at different (extreme) temperatures. Then the capabilities of the determined material constants were examined by comparing the predicted material flow stress with the test data at different temperatures, strains, and strain rate history. The comparison demonstrates that the internal state variable plasticity model can successfully recover dynamic material behavior at various deformation states including the loading path effect. In addition, thermal softening due to adiabatic deformation was also captured by this approach.