ArticleMaterials (Basel, Switzerland)2023
Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- Machine Learning-Based Multi-Objective Composition Optimization of High-Nitrogen Austenitic Stainless Steels.Materials (Basel, Switzerland) · 2025Article
- AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors.PloS one · 2025Article
- Prediction of digital transformation of manufacturing industry based on interpretable machine learning.PloS one · 2024Article
- Prediction of martensite start temperature of steel combined with expert experience and machine learning.Science and technology of advanced materials · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R
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Registered trials
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