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 

机构地区：[1]Electromechanical Engineering Department,University of Technology-Iraq,Baghdad,Iraq

出　　处：《Fluid Dynamics & Materials Processing》2023年第8期2083-2107,共25页流体力学与材料加工（英文）

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 failure.The 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 measurements.In particular,the coefficients of the traditional force law formula are found using relevant numerical methods.It 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 performances.Building strong models from weak models,XGB helps to predict fatigue life by reducing model partiality and variation in supervised learning.Fuzzy neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.

关 键 词：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 

分 类 号：TG146.21[一般工业技术—材料科学与工程] TP181[金属学及工艺—金属材料]