Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

Abstract An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64–8–5–15 manganese brass alloy at the deformation temperature of 873–1073 K and strain rate of 001–10 s⁻¹, the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3139 %, 2448 % and 1265 %, and the values of RMSE were calculated to be 1804, 1482 and 0467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64–8–5–15 alloy.