用人工神经网络模拟合金元素对高碳钢Ms温度的定量影响

EnergyRN: Electrochemical Energy Engineering (EnergyRN) (Topic) Pub Date : 2021-07-20 DOI:10.2139/ssrn.3889918

Xiao-song Wang, P. Narayana, A. K. Maurya, H. Kim, B. Hur, N. Reddy

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引用次数: 0

摘要

化学成分影响钢的性能和马氏体起始温度。利用人工神经网络对高碳钢的Ms温度进行了预测。同时，它使我们能够在相当大的可选择范围内估计合金元素对Ms温度的定量影响，这是第一次准确地发布这样的结果。与以前的公式相比，这个公式简单、直观、精度高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling the Quantitative Effect of Alloying Elements on the Ms Temperature of High Carbon Steel by Artificial Neural Networks

Chemical composition affects the properties and the martensite start (Ms) temperature of steels. This study predicts the Ms temperature of high carbon steel via artificial neural networks. Meanwhile, it enables us to estimate the quantitative effect of alloying elements on the Ms temperature on a sizeable selectable scale, which is the first time to release such results exactly. Compared to the previous formulas, this one is simple, visual, with high accuracy.

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EnergyRN: Electrochemical Energy Engineering (EnergyRN) (Topic)

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