{"title":"Tension anisotropy of rolled AA7075-T6 auminium alloy: Experiments, BP-Neural network modeling and orientation dependent failure mechanisms","authors":"L. Lv, Wei William Lee, Hui Lin, Tao Jin","doi":"10.1166/mex.2024.2716","DOIUrl":null,"url":null,"abstract":"This paper presents the investigations on anisotropic tension mechanical responses of AA7075-T6 based on experiments, classical strength theory, BP-neural network modeling and fracture morphology characterization. Results show that the tension strength anisotropy weakens with deformation\n degree. Compared to the traditional method, the machine learning model exhibits more flexible in solving the anisotropic plastic responses of AA7075-T6 auminium alloy sheet and provides more accurate predictions. Through analyzing the fracture surface of tension specimen at various orientations,\n the failure mechanism is sensitive to orientation. Specifically, the irregular distribution of dimples zones and cleavage steps can be observed at lower material orientation. As the orientation increases, the alternative occurrence of ductile and brittle features dominates the failure mechanism.\n The medium-size dimple caused by coalescence of small-size dimples represents a transition between ductile features zone and brittle characteristics region.","PeriodicalId":18318,"journal":{"name":"Materials Express","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Express","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1166/mex.2024.2716","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Materials Science","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents the investigations on anisotropic tension mechanical responses of AA7075-T6 based on experiments, classical strength theory, BP-neural network modeling and fracture morphology characterization. Results show that the tension strength anisotropy weakens with deformation
degree. Compared to the traditional method, the machine learning model exhibits more flexible in solving the anisotropic plastic responses of AA7075-T6 auminium alloy sheet and provides more accurate predictions. Through analyzing the fracture surface of tension specimen at various orientations,
the failure mechanism is sensitive to orientation. Specifically, the irregular distribution of dimples zones and cleavage steps can be observed at lower material orientation. As the orientation increases, the alternative occurrence of ductile and brittle features dominates the failure mechanism.
The medium-size dimple caused by coalescence of small-size dimples represents a transition between ductile features zone and brittle characteristics region.