用于奥氏体回火球墨铸铁（ADI）硬度预测的遗传算法（GA）-反向传播（BP）网络方法

IF 1.7 4区材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials Science and Technology Pub Date : 2024-06-02 DOI:10.1177/02670836241255240

Pengchun Li, Yuzhou Du, Min Zhang, Qian Yang, Chen Liu, Xin Wang, Ruochen Zhang, Bailing Jiang

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引用次数: 0

摘要

在钢铁工业中，硬度是评估淬火处理成功与否的重要指标，影响着材料的加工性能和磨损性能。在本研究中，利用由 125 个经过不同奥氏体回火热处理参数处理的 QT500-7 样品硬度值组成的数据集，训练了一个预测奥氏体回火球墨铸铁 (ADI) 硬度的神经网络模型。在给定热处理参数的情况下，基于遗传算法和误差反向传播算法建立的模型在预测 ADI 硬度方面表现出很高的准确性。该模型的均方误差约为 1.019，表明该模型在根据指定的热处理参数预测 ADI 硬度方面的可靠性和精确性。

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Genetic algorithm (GA)–backpropagation (BP) network approach for hardness prediction of austempered ductile iron (ADI)

Hardness serves as a crucial indicator for assessing the success of quenching treatment in the steel and iron industry, impacting the processability and wear properties of materials. In the present study, a dataset comprising 125 hardness values of the QT500-7 sample subjected to various austempering heat treatment parameters was utilised to train a neural network model for predicting the hardness of austempered ductile iron (ADI). The established model based on a genetic algorithm and error backpropagation algorithm demonstrates high accuracy in predicting the hardness of ADI if given heat treatment parameters. The mean square error of the model was about 1.019, indicating the reliability and precision of the model in predicting the hardness of ADI based on the specified heat treatment parameters.

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来源期刊

Materials Science and Technology 工程技术-材料科学：综合

CiteScore

2.70

自引率

5.60%

发文量

审稿时长

3 months

期刊介绍：《Materials Science and Technology》（MST） is an international forum for the publication of refereed contributions covering fundamental and technological aspects of materials science and engineering.