Study on the effect of temperature on mechanical behavior based on a neural network plasticity model

This study presents a comprehensive investigation of the mechanical behavior of silicon carbide-reinforced aluminum matrix composites under multiaxial stress states, with particular emphasis on temperature-dependent characteristics in SiC/Al2009 systems. A neural network-based constitutive framework was developed to precisely characterize the material’s thermomechanical responses across varying thermal conditions, effectively revealing the synergistic effects of thermal gradients and complex stress states on yield characteristics and tension-compression anisotropy. Experimental observations demonstrated substantial interaction between plastic strain evolution and thermal activation mechanisms, with distinct necking morphologies emerging at 473 K and significant variations in thermal sensitivity observed across different stress configurations. The proposed computational model demonstrated enhanced predictive accuracy compared to conventional approaches when handling coupled strain-temperature loading scenarios. The investigation further identified pronounced tension-compression asymmetry in yield behavior, which exhibited a strong correlation with thermal-strain coupling effects. The optimized yield criterion effectively captured strain hardening patterns under multiaxial stress conditions, demonstrating superior predictive capability for complex loading paths.