J. Wertz, M. Cherry, Laura Homa, Nick Lorenzo, E. Blasch
{"title":"Multi-Scale Mixed Modality Microstructure Assessment for Titanium (M4AT) Data","authors":"J. Wertz, M. Cherry, Laura Homa, Nick Lorenzo, E. Blasch","doi":"10.32548/rs.2022.040","DOIUrl":null,"url":null,"abstract":"The capability of a material depends on multiscale physical properties. In many cases, state-of-the-art material characterization methods for micro-to-mesoscale features require extensive preparation or destructive analysis. These shortcomings limit their use for quality control of component-scale parts, as extensive preparation or destructive analysis are prohibitively expensive or impossible for real-time assessment. One example is the detection and characterization of critical microtexture regions in titanium, where the state-of-the-art sensing method is both damaging and constrained to a laboratory environment. New sensing approaches that achieve the capability of laboratory-based characterization methods without destructive assessment offer promise for manufacturing, inspection, and assembly. A potential solution is to develop novel data fusion algorithms to compliment existing nondestructive evaluation techniques.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASNT 30th Research Symposium Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32548/rs.2022.040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The capability of a material depends on multiscale physical properties. In many cases, state-of-the-art material characterization methods for micro-to-mesoscale features require extensive preparation or destructive analysis. These shortcomings limit their use for quality control of component-scale parts, as extensive preparation or destructive analysis are prohibitively expensive or impossible for real-time assessment. One example is the detection and characterization of critical microtexture regions in titanium, where the state-of-the-art sensing method is both damaging and constrained to a laboratory environment. New sensing approaches that achieve the capability of laboratory-based characterization methods without destructive assessment offer promise for manufacturing, inspection, and assembly. A potential solution is to develop novel data fusion algorithms to compliment existing nondestructive evaluation techniques.