Damage mechanism identification in composites via machine learning and acoustic emission

作     者：C.Muir B.Swaminathan A.S.Almansour K.Sevener C.Smith M.Presby J.D.Kiser T.M.Pollock S.Daly 

作者机构：Materials DepartmentUniversity of California-Santa BarbaraSanta BarbaraCAUSA NASA Glenn Research CenterClevelandOHUSA Materials Scienceand Engineering DepartmentUniversity of Michigan-Ann ArborAnn ArborMIUSA Mechanical Engineering DepartmentUniversity of California-Santa BarbaraSanta BarbaraCAUSA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：852-866页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CM.and B.S.gratefully acknowledge financial support from the NASA Spce Tochnology Gaduate Research Opportunites Felowship(Grants:8ONSSC19K1164 and 8ONSSC17K0084,SD.and T.MP.gratefully acknowiedge fnanchl support from the Natonal Sclonce Found ation Uward 1984641) patt of the HDR IDEAS Insatute.The authors additonally thank Aaron Engel for the suggeston for this project and Dr Neal Brodnik for an Introduction to tSNE 

主　　题：composites composite mechanism 

摘      要：Damage mechanism identification has scientific and practical ramifications for the structural health monitoring,design,and application of composite *** advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage *** review evaluates the state of the field,beginning with a physics-based understanding of acoustic emission waveform feature extraction,followed by a detailed overview of waveform clustering,labeling,and error analysis *** requirements for damage mechanism identification in any machine learning framework,including those currently in use,under development,and yet to be explored,are discussed.