A thermal damage-coupled constitutive model for predicting fracture and microstructure evolution and its application in the hot spinnability process

Abstract

Although different damage-coupled physically based models have been proposed, those damage models are established based on the isothermal uniaxial or multi-axial tensile condition. However, the influence of the stress state on ductile failure strain is not adequately considered in the thermal damage models. It is widely recognized that the stress state has significant effect on damage evolution and ductility fracture for metallic materials. Actually, the current damage-coupled physically based models are not suitable for complex hot forming and the applications of these damage-coupled models are mainly focused on sheet hot stamping forming to date. Therefore, a modified damage-coupled unified model considering a broad range of stress states was established to figure out the complex damage behavior and microstructure evolution of metallic materials during the thermal deformation process. In this study, the Mg-6Gd-5Y-0.3Zr alloy was used as the experimental material. First, the flow behavior of the alloy was explored using a series of experiments under various stress states (i.e., uniaxial tensile, shear and compression) under various combinations of strain rates and temperatures. On this basis, a modified damage-coupled physics-based model was developed, in which the dynamic recrystallization, damage, stress triaxiality and Lode parameter were included as internal state variables. Furthermore, the model was integrated into ABAQUS to verify the applicability using hot spinnability test because the stress state was very complex during the forming process. The comparison between the calculated results and experiments is carried out. According to the simulation result, the predicted ultimate thinning rate is 71.14%, with an error of only 7.85% from the experimentally measured ultimate thinning rate of 77.2%, and the correlation coefficient and error for thickness and profile between the predicted values and experimental values are 0.9980 and 5.05%, respectively. These demonstrate that the developed model is reliable.