用反向传播人工神经网络模拟合金元素和热处理参数对热模具钢力学性能的影响

IF 3.1 2区 材料科学 Q1 METALLURGY & METALLURGICAL ENGINEERING
Yong Liu , Jing-chuan Zhu , Yong Cao
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引用次数: 16

摘要

材料数据深度挖掘是材料基因组探索的重要内容。为了对热模型钢材料数据进行深度挖掘,获得合金元素、热处理参数与材料性能之间的关系,建立了一个11 × 12 × 12 × 4反向传播(BP)人工神经网络(ANN)。输入合金元素含量、淬火回火温度;输出参数为硬度、抗拉强度和屈服强度。人工神经网络具有较高的拟合精度。利用该模型研究了合金元素和热处理参数对热模具钢性能的影响。结果表明:高温硬度随C、Si、Mo、W、Ni、V、Cr合金元素含量的增加而增大,达到最大值,随合金元素含量的增加而减小;人工神经网络还预测,高温硬度随淬火温度的升高而降低,随回火温度的升高而达到最优值。该模型为新型热模具钢的设计提供了新的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling effects of alloying elements and heat treatment parameters on mechanical properties of hot die steel with back-propagation artificial neural network

Materials data deep-excavation is very important in materials genome exploration. In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements, heat treatment parameters and materials properties, a 11 × 12 × 12 × 4 back-propagation (BP) artificial neural network (ANN) was set up. Alloying element contents, quenching and tempering temperatures were selected as input; hardness, tensile and yield strength were set as output parameters. The ANN shows a high fitting precision. The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model. The results indicate that high temperature hardness increases with increasing alloying element content of C, Si, Mo, W, Ni, V and Cr to a maximum value and decreases with further increase in alloying element content. The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature, and possess an optimal value with increasing tempering temperature. This model provides a new tool for novel hot die steel design.

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来源期刊
CiteScore
4.30
自引率
0.00%
发文量
2879
审稿时长
3.0 months
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