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Article

Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models

by
Muhammad Ishtiaq
1,
Hafiz Muhammad Rehan Tariq
2,
Devarapalli Yuva Charan Reddy
3,
Sung-Gyu Kang
1,* and
Nagireddy Gari Subba Reddy
4,*
1
Multiscale Structural Materials Laboratory, School of Materials Science and Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea
2
Department of Mechanical Engineering, Incheon National University, Incheon 22012, Republic of Korea
3
Department of Artificial Intelligence and Machine Learning (AI&ML), Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad 500075, Telangana, India
4
Virtual Materials Laboratory, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea
*
Authors to whom correspondence should be addressed.
Metals 2025, 15(3), 288; https://doi.org/10.3390/met15030288
Submission received: 11 February 2025 / Revised: 1 March 2025 / Accepted: 4 March 2025 / Published: 6 March 2025

Abstract

The creep rupture life of 5Cr-0.5Mo steels used in high-temperature applications is significantly influenced by factors such as minor alloying elements, hardness, austenite grain size, non-metallic inclusions, service temperature, and applied stress. The relationship of these variables with the creep rupture life is quite complex. In this study, the creep rupture life of 5Cr-0.5Mo steel was predicted using various machine learning (ML) models. To achieve higher accuracy, various ML techniques, including random forest (RF), gradient boosting (GB), linear regression (LR), artificial neural network (ANN), AdaBoost (AB), and extreme gradient boosting (XGB), were applied with careful optimization of hidden parameters. Among these, the ANN-based model demonstrated superior performance, yielding high accuracy with minimal prediction errors for the test dataset (RMSE = 0.069, MAE = 0.053, MAPE = 0.014, and R2 = 1). Additionally, we developed a user-friendly graphical user interface (GUI) for the ANN model, enabling users to predict and optimize creep rupture life. This tool helps materials scientists and industrialists prevent failures in high-temperature applications and design steel compositions with enhanced creep resistance.
Keywords: 5Cr-0.5Mo steel; creep rupture life; machine learning; composition; temperature; stress 5Cr-0.5Mo steel; creep rupture life; machine learning; composition; temperature; stress

Share and Cite

MDPI and ACS Style

Ishtiaq, M.; Tariq, H.M.R.; Reddy, D.Y.C.; Kang, S.-G.; Reddy, N.G.S. Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models. Metals 2025, 15, 288. https://doi.org/10.3390/met15030288

AMA Style

Ishtiaq M, Tariq HMR, Reddy DYC, Kang S-G, Reddy NGS. Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models. Metals. 2025; 15(3):288. https://doi.org/10.3390/met15030288

Chicago/Turabian Style

Ishtiaq, Muhammad, Hafiz Muhammad Rehan Tariq, Devarapalli Yuva Charan Reddy, Sung-Gyu Kang, and Nagireddy Gari Subba Reddy. 2025. "Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models" Metals 15, no. 3: 288. https://doi.org/10.3390/met15030288

APA Style

Ishtiaq, M., Tariq, H. M. R., Reddy, D. Y. C., Kang, S.-G., & Reddy, N. G. S. (2025). Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models. Metals, 15(3), 288. https://doi.org/10.3390/met15030288

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