Prediction and Analysis of Creep Rupture Life of 9Cr Martensitic-Ferritic Heat-Resistant Steel by Neural Networks.

Muhammad Ishtiaq, Seungmin Hwang, Won-Seok Bang, Sung-Gyu Kang, Nagireddy Gari Subba Reddy

Abstract read

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Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Muhammad IshtiaqDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501, Jinju 52828, Republic of Korea.ORCID 0000-0002-4566-9467

Seungmin HwangDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501, Jinju 52828, Republic of Korea.

Won-Seok BangDepartment of Business Administration, Entrepreneurship Research Institute, Gyeongsang National University, 501, Jinju 52828, Republic of Korea.

Sung-Gyu KangDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501, Jinju 52828, Republic of Korea.

Nagireddy Gari Subba ReddyDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, 501, Jinju 52828, Republic of Korea.ORCID 0000-0003-4206-4515

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thermal and nuclear power systems require materials capable of sustaining high mechanical and thermal loads over prolonged service durations. Among these, 9Cr heat-resistant steels are particularly attractive due to their superior mechanical strength and extended creep rupture life, making them suitable for extreme environments. In this study, multiple machine learning models were explored to predict the creep rupture life of 9Cr heat-resistant steels. A comprehensive dataset of 913 samples, compiled from experimental results and literature, included eight input variables-covering chemical composition, stress, and temperature-and one output variable, the creep rupture life. The optimized artificial neural network (ANN) model achieved the highest predictive accuracy with a regularization coefficient of 0.01, 10,000 training iterations, and five hidden layers with 30 neurons per layer, attaining an R

Indexed as

9Cr heat-resistant steelartificial neural networkcreep rupturepredictionquantitative estimation

Identifiers

PMID41597967

DOI10.3390/ma19020257

PMCPMC12842878

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.