High-temperature tensile tests were developed to explore the flow features of an Al-Zn-Mg-Cu alloy. The fracture characteristics and microstructural evolution mechanisms were thoroughly revealed. The results demonstrated that both intergranular fractures and ductile fractures occurred, which affected the hot tensile fracture mechanism. During high-temperature tensile, the second phase (Al₂CuMg) at the grain boundaries (GBs) promoted the formation and accumulation of dimples. With the continual progression of high-temperature tensile, the aggregation/coarsening of dimples along GBs appear, aggravating the intergranular fracture. The coalescence and coarsen of dimples are reinforced at higher tensile temperatures or lower strain rates. Considering the impact of microstructural evolution and dimple formation/coarsening on tensile stresses, a physical mechanism constitutive (PMC) equation is herein proposed. According to the validation and analysis, the predictive results were in preferable accordance with the testing data, showing the outstanding reconfiguration capability of the PMC model for high-temperature tensile features in Al–Zn–Mg–Cu alloys.

The AA7075 holds significant importance in the aerospace field. Understanding its microstructure evolution and constitutive relationships during warm deformation is crucial for optimizing forming processes. To this end, isothermal compression experiments were conducted at different temperatures and strain rates to analyze their flow stress behavior. The microstructure evolution was characterized using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). Microstructural analysis confirmed that dynamic recovery constitutes the predominant softening mechanism under warm forming conditions. The results indicate that flow stress is highly sensitive to deformation parameters, decreasing with increasing temperature and rising with increasing strain rate. To accurately describe the flow behavior, two distinct constitutive models were formulated: (1) a phenomenological Hensel–Spittel–Garofalo (HSG) model; (2) a novel hybrid machine-learning model that innovatively integrates the Harris Hawks Optimization (HHO) algorithm with an LSTM model. Both constitutive models demonstrate reasonable predictive accuracy. In comparison, the HHO-LSTM model demonstrated a superior ability to capture complex nonlinear relationships, achieving highly precise predictions of flow stress across the full range of deformation conditions tested in this work. The hybrid machine-learning model proposed in this study provides a highly accurate method for describing and predicting the flow behavior of the AA7075 during warm forming, offering a powerful predictive tool for engineering applications.