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 HMn64-8-5-1.5 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 0.01-10 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, 3.139 %, 2.448 % and 1.265 %, and the values of RMSE were calculated to be 1.804, 1.482 and 0.467 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 HMn64-8-5-1.5 alloy.

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“…The BP neural network model randomly assigns weights and thresholds, which have many variable parameters, leading to unstable model computation [34]. The model prediction performance can be improved by optimizing the BP neural network using the GWO [35]. However, the GWO algorithm has the problems of uneven initial population distribution, slow convergence speed, and easily falling into local optimization.…”

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