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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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 20716 - 16
Hauptverfasser:	Zou, Fan, Liu, Pengjie, Chen, Yanzhan, Zhao, Yaohua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 05.09.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	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
Online-Zugang:	Volltext
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