Machine learning-assisted creep life prediction and empirical formula generation for 9-12% Cr steel

IF 5.3 2区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Scripta Materialia Pub Date : 2024-11-30 DOI:10.1016/j.scriptamat.2024.116480

Yifan Wu , Jingzi Zhang , Chengquan Zhong , Jiakai Liu , Kailong Hu , Xi Lin

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引用次数: 0

Abstract

The creep behavior of steels is influenced by factors such as lattice structure, defects, and stress conditions. Given the high cost and time required for creep tests, accurately predicting creep life and minimum creep rate is essential. This study analyzed 9–12% chromium (Cr) steel using a dataset of 1496 entries covering material composition, mechanical properties, minimum creep rate, and creep life. Nine machine learning (ML) models were developed, with the artificial neural network (ANN) achieving the highest prediction accuracy, evidenced by a coefficient of determination (R²) of 0.9973 and a root mean square error (RMSE) of 0.045. A dual-target neural network model provided R² values of 0.9853 for creep life and 0.9838 for minimum creep rate. Additionally, an empirical equation based on gene expression programming (GEP) achieved an R² exceeding 0.9741. This study offers novel insights into the design of 9–12% Cr steel with enhanced creep life.

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来源期刊

Scripta Materialia 工程技术-材料科学：综合

CiteScore

11.40

自引率

5.00%

发文量

581

审稿时长

34 days

期刊介绍： Scripta Materialia is a LETTERS journal of Acta Materialia, providing a forum for the rapid publication of short communications on the relationship between the structure and the properties of inorganic materials. The emphasis is on originality rather than incremental research. Short reports on the development of materials with novel or substantially improved properties are also welcomed. Emphasis is on either the functional or mechanical behavior of metals, ceramics and semiconductors at all length scales.