{"title":"Damage mechanism identification and failure behavior of 2D needle-punched SiCf/SiC composites based on acoustic emission and digital image correlation","authors":"","doi":"10.1016/j.matchar.2024.114491","DOIUrl":null,"url":null,"abstract":"<div><div>In order to improve the reliability of damage analysis for SiC<sub>f</sub>/SiC composites, an identification method of damage mechanism was established by combining in-situ acoustic emission (AE) and digital image correlation (DIC). The corresponding failure behavior of 2D needle-punched SiC<sub>f</sub>/SiC composites during ambient-temperature tensile test was investigated in detail. Through a machine learning <em>k</em>-means algorithm, AE signals could be effectively divided into five clusters: friction and sliding, interface damage, matrix cracking, individual fiber breaks and collective fiber breaks. DIC results show that the surface strain of composites increased non-uniformly during the tensile process, and the architecture of the composites had a significant influence on the initiation and propagation of cracks. To summarize, the tensile process consisted of three stages: the elastic stage, the rapid propagation of matrix cracks, the coordinated fiber fracture within the large strain bands. The failure of composites was dominated by the limited load transferring ability of the interface.</div></div>","PeriodicalId":18727,"journal":{"name":"Materials Characterization","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Characterization","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1044580324008726","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
Abstract
In order to improve the reliability of damage analysis for SiCf/SiC composites, an identification method of damage mechanism was established by combining in-situ acoustic emission (AE) and digital image correlation (DIC). The corresponding failure behavior of 2D needle-punched SiCf/SiC composites during ambient-temperature tensile test was investigated in detail. Through a machine learning k-means algorithm, AE signals could be effectively divided into five clusters: friction and sliding, interface damage, matrix cracking, individual fiber breaks and collective fiber breaks. DIC results show that the surface strain of composites increased non-uniformly during the tensile process, and the architecture of the composites had a significant influence on the initiation and propagation of cracks. To summarize, the tensile process consisted of three stages: the elastic stage, the rapid propagation of matrix cracks, the coordinated fiber fracture within the large strain bands. The failure of composites was dominated by the limited load transferring ability of the interface.
期刊介绍:
Materials Characterization features original articles and state-of-the-art reviews on theoretical and practical aspects of the structure and behaviour of materials.
The Journal focuses on all characterization techniques, including all forms of microscopy (light, electron, acoustic, etc.,) and analysis (especially microanalysis and surface analytical techniques). Developments in both this wide range of techniques and their application to the quantification of the microstructure of materials are essential facets of the Journal.
The Journal provides the Materials Scientist/Engineer with up-to-date information on many types of materials with an underlying theme of explaining the behavior of materials using novel approaches. Materials covered by the journal include:
Metals & Alloys
Ceramics
Nanomaterials
Biomedical materials
Optical materials
Composites
Natural Materials.