Prediction of low-temperature impact property of 35CrMoA steel after heat treatment based on BP neural network

doi:10.13251/j.issn.0254-6051.2025.03.020

Abstract

Abstract: In order to predict the low-temperature impact performance at -45 ℃ of normalized 35CrMoA steel test bars after subcritical quenching and high-temperature tempering, the actual state parameters of low-temperature impact property at -45 ℃ under different heat treatment temperatures were used as learning specimens to train and predict the low-temperature impact property using a three-layer back propagation artificial neural network (BPANN). The results show that the BPANN can predict the low-temperature impact property at -45 ℃ of the 35CrMoA steel specimens subcritical quenched at different temperatures and tempered at different temperatures, with an error range of 5% to 9%. The predicted values of the BPANN are lower than the actual values, and the prediction accuracy can be improved by increasing the training specimens. The trend of the predicted data is the same as that of the measured data, and it can achieve a meaningful prediction. This study can reduce the number of tests in actual production through prediction, save test costs, and is helpful for the research on the low-temperature impact property prediction of the 35CrMoA steel at other subcritical quenching temperatures.

Key words: 35CrMoA steel, BP neural network, thermal stability, subcritical quenching, prediction

CLC Number:

TG156

Jin Shuai, Wang Huiqiang, Cui Jianying, Wang Yanshan, Yan Xuelan, Li Wenxiang. Prediction of low-temperature impact property of 35CrMoA steel after heat treatment based on BP neural network[J]. Heat Treatment of Metals, 2025, 50(3): 125-131.

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