Jeonghun Shin, Sanghoon Kang, Cheolhee Kim, Sukjoon Hong, Minjung Kang
{"title":"基于多传感器和深度学习的al6000合金激光堆焊凝固裂纹识别","authors":"Jeonghun Shin, Sanghoon Kang, Cheolhee Kim, Sukjoon Hong, Minjung Kang","doi":"10.2351/7.0001112","DOIUrl":null,"url":null,"abstract":"Solidification cracking, one of the most critical weld defects in laser welding of Al 6000 alloys, occurs at the final stage of solidification owing to shrinkage of the weld metal and deteriorates the joint strength and integrity. The filler metal can control the chemical composition of the weld metal, which mitigates solidification cracking. However, the chemical composition is difficult to control in autogenous laser welding. Temporal and spatial laser beam modulations have been introduced to control solidification cracking in autogenous laser welding because weld morphology is one of the factors that influences the initiation and propagation of solidification cracking. Solidification cracks generate thermal discontinuities and visual flaws on the bead surface. In this study, a high-speed infrared camera and a coaxial charge-coupled device camera with an auxiliary illumination laser (808 nm) were employed to identify solidification cracking during laser welding. Deep learning models, developed using two sensor images of a solidified bead, provided location-wise crack formation information. The multisensor-based convolutional neural network models achieved an impressive accuracy of 99.31% in predicting the crack locations. Thus, applying deep learning models expands the capability of predicting solidification cracking, including previously undetectable internal cracks.","PeriodicalId":50168,"journal":{"name":"Journal of Laser Applications","volume":"52 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of solidification cracking using multiple sensors and deep learning in laser overlap welded Al 6000 alloy\",\"authors\":\"Jeonghun Shin, Sanghoon Kang, Cheolhee Kim, Sukjoon Hong, Minjung Kang\",\"doi\":\"10.2351/7.0001112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Solidification cracking, one of the most critical weld defects in laser welding of Al 6000 alloys, occurs at the final stage of solidification owing to shrinkage of the weld metal and deteriorates the joint strength and integrity. The filler metal can control the chemical composition of the weld metal, which mitigates solidification cracking. However, the chemical composition is difficult to control in autogenous laser welding. Temporal and spatial laser beam modulations have been introduced to control solidification cracking in autogenous laser welding because weld morphology is one of the factors that influences the initiation and propagation of solidification cracking. Solidification cracks generate thermal discontinuities and visual flaws on the bead surface. In this study, a high-speed infrared camera and a coaxial charge-coupled device camera with an auxiliary illumination laser (808 nm) were employed to identify solidification cracking during laser welding. Deep learning models, developed using two sensor images of a solidified bead, provided location-wise crack formation information. The multisensor-based convolutional neural network models achieved an impressive accuracy of 99.31% in predicting the crack locations. Thus, applying deep learning models expands the capability of predicting solidification cracking, including previously undetectable internal cracks.\",\"PeriodicalId\":50168,\"journal\":{\"name\":\"Journal of Laser Applications\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Laser Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2351/7.0001112\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Laser Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2351/7.0001112","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Identification of solidification cracking using multiple sensors and deep learning in laser overlap welded Al 6000 alloy
Solidification cracking, one of the most critical weld defects in laser welding of Al 6000 alloys, occurs at the final stage of solidification owing to shrinkage of the weld metal and deteriorates the joint strength and integrity. The filler metal can control the chemical composition of the weld metal, which mitigates solidification cracking. However, the chemical composition is difficult to control in autogenous laser welding. Temporal and spatial laser beam modulations have been introduced to control solidification cracking in autogenous laser welding because weld morphology is one of the factors that influences the initiation and propagation of solidification cracking. Solidification cracks generate thermal discontinuities and visual flaws on the bead surface. In this study, a high-speed infrared camera and a coaxial charge-coupled device camera with an auxiliary illumination laser (808 nm) were employed to identify solidification cracking during laser welding. Deep learning models, developed using two sensor images of a solidified bead, provided location-wise crack formation information. The multisensor-based convolutional neural network models achieved an impressive accuracy of 99.31% in predicting the crack locations. Thus, applying deep learning models expands the capability of predicting solidification cracking, including previously undetectable internal cracks.
期刊介绍:
The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety.
The following international and well known first-class scientists serve as allocated Editors in 9 new categories:
High Precision Materials Processing with Ultrafast Lasers
Laser Additive Manufacturing
High Power Materials Processing with High Brightness Lasers
Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures
Surface Modification
Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology
Spectroscopy / Imaging / Diagnostics / Measurements
Laser Systems and Markets
Medical Applications & Safety
Thermal Transportation
Nanomaterials and Nanoprocessing
Laser applications in Microelectronics.