Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy

The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 20716 - 16
Hlavní autoři: Zou, Fan, Liu, Pengjie, Chen, Yanzhan, Zhao, Yaohua
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 05.09.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/166/988
639/301/1034/1037
Accuracy
Algorithms
Alloys
Artificial intelligence
Creep property prediction
Datasets
Humanities and Social Sciences
Learning algorithms
Life span
Machine learning
multidisciplinary
Neural networks
Nickel
Optimization algorithms
Physical properties
Prediction models
R&D
Research & development
Rupture
Science
Science (multidisciplinary)
Shapley additive explanations
Single crystals
Sparrow optimization algorithm
Superalloys
Temperature
XGBoost
Sparrow optimization algorithm
Creep property prediction
Shapley additive explanations
XGBoost
ISSN:2045-2322, 2045-2322
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Shrnutí:The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-71431-1