Guang Yang , Yilian Xie , Shuo Zhao , Lanyun Qin , Xiangming Wang , Bin Wu
{"title":"Quality Control: Internal Defects Formation Mechanism of Selective Laser Melting Based on Laser-powder-melt Pool Interaction: A Review","authors":"Guang Yang , Yilian Xie , Shuo Zhao , Lanyun Qin , Xiangming Wang , Bin Wu","doi":"10.1016/j.cjmeam.2022.100037","DOIUrl":null,"url":null,"abstract":"<div><p>Selective laser melting (SLM) is a 3D printing technology with a high near-net-shape ability and forming accuracy. However, the inevitable internal defects significantly hinder its development. Therefore, it is essential to fully understand the causes of internal defects in SLM processing and minimize the defects to achieve quality control accordingly. This work reviews the recent studies on internal defects in SLM, presenting the main internal defects of SLM as impurities, lack of fusion, gas pores, and micro-crack. These internal defects occur on the various phenomena in the laser-powder-melt pool (LPMP) stage. The formation of SLM internal defects is mainly affected by oxidation, denudation, balling, spatter, and keyholes; here, balling, spattering, and the keyhole phenomenon are the main factors causing internal defects in LPMP. Hence, this paper focuses on reviewing the balling effect, spatter behavior, and keyhole phenomenon, introducing the action mechanism of the above three phenomena under different process conditions. Additionally, the spatter behavior when forming internal defects is proposed. This review also considers the correlation between the spatter behavior and keyhole phenomenon and makes an important contribution to understanding and reducing SLM internal defects. It presents a reliable opinion on real-time monitoring and machine intelligent learning for SLM processing in the future, as well as supporting a systematic thinking for the suppression of defect formation in SLM.</p></div>","PeriodicalId":100243,"journal":{"name":"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers","volume":"1 3","pages":"Article 100037"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266572200023X/pdfft?md5=48e00c57b137ea26e0311b6a41049fb5&pid=1-s2.0-S277266572200023X-main.pdf","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S277266572200023X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
Selective laser melting (SLM) is a 3D printing technology with a high near-net-shape ability and forming accuracy. However, the inevitable internal defects significantly hinder its development. Therefore, it is essential to fully understand the causes of internal defects in SLM processing and minimize the defects to achieve quality control accordingly. This work reviews the recent studies on internal defects in SLM, presenting the main internal defects of SLM as impurities, lack of fusion, gas pores, and micro-crack. These internal defects occur on the various phenomena in the laser-powder-melt pool (LPMP) stage. The formation of SLM internal defects is mainly affected by oxidation, denudation, balling, spatter, and keyholes; here, balling, spattering, and the keyhole phenomenon are the main factors causing internal defects in LPMP. Hence, this paper focuses on reviewing the balling effect, spatter behavior, and keyhole phenomenon, introducing the action mechanism of the above three phenomena under different process conditions. Additionally, the spatter behavior when forming internal defects is proposed. This review also considers the correlation between the spatter behavior and keyhole phenomenon and makes an important contribution to understanding and reducing SLM internal defects. It presents a reliable opinion on real-time monitoring and machine intelligent learning for SLM processing in the future, as well as supporting a systematic thinking for the suppression of defect formation in SLM.