首页|Research Data from Central South University Update Understanding of Machine Lear ning (Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy)
Research Data from Central South University Update Understanding of Machine Lear ning (Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting out of Central South University by NewsRx editors, research stated, “The evaluation of creep rupture life is compl ex due to its variable formation mechanism.” Financial supporters for this research include Fundamental Research Funds For Th e Central Universities of Central South University. The news journalists obtained a quote from the research from Central South Unive rsity: “In this paper, machine learning algorithms are applied to explore the cr eep rupture life span as a function of 27 physical properties to address this is sue. By training several classical machine learning models and comparing their p rediction performance, XGBoost is finally selected as the predictive model for c reep rupture life. Moreover, we introduce an interpretable method, Shapley addit ive explanations (SHAP), to explain the creep rupture life predicted by the XGBo ost model. The SHAP values are then calculated, and the feature importance of th e creep rupture life yielded by the XGBoost model is discussed. Finally, the cre ep fracture life is optimized by using the chaotic sparrow optimization algorith m. We then show that our proposed method can accurately predict and optimize cre ep properties in a cheaper and faster way than other approaches in the experimen ts.”
Central South UniversityCyborgsEmerg ing TechnologiesMachine Learning
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