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首页> 外文期刊>Journal of Materials Science >Artificial neural network prediction of heat-treatment hardness and abrasive wear resistance of High-Vanadium High-Speed Steel (HVHSS)
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Artificial neural network prediction of heat-treatment hardness and abrasive wear resistance of High-Vanadium High-Speed Steel (HVHSS)

机译：人工神经网络预测高钒高速钢（HVHSS）的热处理硬度和耐磨性

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摘要

The hardness and abrasive wear resistance were measured after High-Vanadium High-Speed Steel (HVHSS) were quenched at 900 degrees C-1100 degrees C, and then tempered at 250 degrees C-600 degrees C. Via one-hidden-layer and two-hidden-layer Back-Propagation (BP) neural networks, the non-linear relationships of hardness (H) and abrasive wear resistance (e) vs. quenching temperature and tempering temperature (T1, T2) were established, respectively, on the base of the experimental data. The results show that the well-trained two-hidden-layer networks have rather smaller training errors and much better generalization performance compared with well-trained one-hidden-layer neural networks, and can precisely predict hardness and abrasive wear resistance according to quenching and tempering temperatures. The prediction values have sufficiently mined the basic domain knowledge of heat treatment process of HVHSS. Therefore, a new way of predicting hardness and wear resistance according to heat treatment technique was provided by the authors.
机译：在将高钒高速钢（HVHSS）在900摄氏度至1100摄氏度的温度下淬火，然后在250摄氏度至600摄氏度的温度回火之后，测量硬度和耐磨性。隐层反向传播（BP）神经网络的基础上，分别建立了硬度（H）和耐磨性（e）与淬火温度和回火温度（T1，T2）的非线性关系实验数据。结果表明，与训练有素的单层神经网络相比，训练有素的两层网络具有较小的训练误差和更好的泛化性能，并且可以根据淬火和淬火来精确预测硬度和耐磨性。回火温度。预测值充分挖掘了HVHSS热处理过程的基本领域知识。因此，作者提供了一种根据热处理技术预测硬度和耐磨性的新方法。

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