Dominik Rauner, Daniel Wolf, Lukas Spano, Michael F. Zaeh
{"title":"A method for the predictive and automated detection of the shrink line location during the powder bed fusion of metals using a laser beam","authors":"Dominik Rauner, Daniel Wolf, Lukas Spano, Michael F. Zaeh","doi":"10.1016/j.procir.2024.08.240","DOIUrl":null,"url":null,"abstract":"<div><div>The powder bed fusion of metals using a laser beam enables the additive manufacturing of topology-optimized parts involving structural transitions and rapid cross-sectional changes. Both geometry features can cause shrink lines, which reduce the dimensional accuracy and the fatigue resistance of the manufactured part. To provide reduction measures, their point of origin needs to be located in advance. This work presents an algorithm capable of automatically predicting the shrink line location for arbitrary discretized geometries. The results demonstrate the reliable detection and layer-wise characterization of the shrink-line-causing geometry features. Suitable discretization parameters were derived and the dependence of the computational time on the part complexity was quantified.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827124008072","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 powder bed fusion of metals using a laser beam enables the additive manufacturing of topology-optimized parts involving structural transitions and rapid cross-sectional changes. Both geometry features can cause shrink lines, which reduce the dimensional accuracy and the fatigue resistance of the manufactured part. To provide reduction measures, their point of origin needs to be located in advance. This work presents an algorithm capable of automatically predicting the shrink line location for arbitrary discretized geometries. The results demonstrate the reliable detection and layer-wise characterization of the shrink-line-causing geometry features. Suitable discretization parameters were derived and the dependence of the computational time on the part complexity was quantified.