Analysis of Prediction Mechanisms and Feature Importance of Martensite Start Temperature of Alloy Steel via Explainable Artificial Intelligence

被引：2

作者：

Jeon, Junhyub ^{[1
]}

Seo, Namhyuk ^{[1
]}

Jung, Jae-Gil ^{[1
,2
]}

Son, Seung Bae ^{[1
,2
]}

Lee, Seok-Jae ^{[1
,2
]}

机构：

[1] Jeonbuk Natl Univ, Div Adv Mat Engn, Jeonju 54896, South Korea

[2] Jeonbuk Natl Univ, Res Ctr Adv Mat Dev, Jeonju 54896, South Korea

来源：

MATERIALS TRANSACTIONS | 2023年 / 64卷 / 09期

关键词：

machine learning; martensite start temperature; explainable artificial intelligence; prediction mechanism; alloy steels; AUSTENITE GRAIN-SIZE; NEURAL-NETWORK; TRANSFORMATION; ELEMENTS;

D O I：

10.2320/matertrans.MT-MI2022004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This study proposes a machine learning model to predict the martensite start temperature (Ms) of alloy steels. We collected 219 usable data from the literature, and adjusted the hyperparameters to propose an accurate machine learning model. Artificial neural networks (ANN) exhibited the best performance compared with existing empirical equation. The prediction mechanisms and feature importance of the ANN with regards to the whole system were discussed via the Shapley additive explanation (SHAP).

引用

页码：2196 / 2201

页数：6

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