Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

作     者：Mustafa Sami Abdullatef Faten NAlzubaidi Anees Al-Tamimi Yasser Ahmed Mahmood 

作者机构：Electromechanical Engineering DepartmentUniversity of Technology-IraqBaghdadIraq 

出 版 物：《Fluid Dynamics & Materials Processing》 (流体力学与材料加工（英文）)

年 卷 期：2023年第19卷第8期

页      面：2083-2107页

核心收录：

学科分类：0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0806[工学-冶金工程] 08[工学] 081104[工学-模式识别与智能系统] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Department of Electromechanical Engineering 

主　　题：Fatigue life high strength aluminum alloy 2090-T83 neuro-fuzzy tree boosting model neural networks adaptive neuro-fuzzy inference systems random forest support vector machines 

摘      要：The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue *** fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios(R?1).Artificial neural networks(ANN),adaptive neuro-fuzzy inference systems(ANFIS),support-vector machines(SVM),a random forest model(RF),and an extreme-gradient tree-boosting model(XGB)are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress *** particular,the coefficients of the traditional force law formula are found using relevant numerical *** is shown that,in comparison to traditional approaches,the neural network and neuro-fuzzy models produce better results,with the neural network models trained using the boosting iterations technique providing the best *** strong models from weak models,XGB helps to predict fatigue life by reducing model partiality and variation in supervised *** neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.