Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud
ABSTRACTWire and Arc Additive Manufacturing (WAAM) with high efficiency and low-cost is an economical choice for the rapid fabrication of medium-to-large-sized metallic components and has attracted great attention from scholars and entrepreneurs in recent years. However, defects such as porosity, an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2023.2294336 |
| _version_ | 1797383117446578176 |
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| author | Mengru Liu Xingwang Bai Shengxuan Xi Honghui Dong Runsheng Li Haiou Zhang Xiangman Zhou |
| author_facet | Mengru Liu Xingwang Bai Shengxuan Xi Honghui Dong Runsheng Li Haiou Zhang Xiangman Zhou |
| author_sort | Mengru Liu |
| collection | DOAJ |
| description | ABSTRACTWire and Arc Additive Manufacturing (WAAM) with high efficiency and low-cost is an economical choice for the rapid fabrication of medium-to-large-sized metallic components and has attracted great attention from scholars and entrepreneurs in recent years. However, defects such as porosity, and humps, could occur occasionally after each layer of deposition on weld bead surfaces due to disturbances and process abnormities. Detection and quantitative evaluation of weld bead defects is crucial to ensure successful deposition and the quality of the entire component. In this paper, a novel defect detection and evaluation system was developed for WAAM utilizing machine vision technology. The system incorporated new defect detection algorithms based on analysing the 2D curvature of the weld bead height curve and the 3D curvature of the weld bead point cloud. Furthermore, a defect evaluation algorithm was developed based on reconstructing the normal weld bead contour using geometric features extracted from the accumulated point cloud. This system enables the automatic detection of weld bead morphology during the WAAM process, offering important information about the location, type, and volume of defects for effective interlayer repairs and enhanced part quality. |
| first_indexed | 2024-03-08T21:16:11Z |
| format | Article |
| id | doaj.art-4b28b1422792408c9d9a0a2ed305f522 |
| institution | Directory Open Access Journal |
| issn | 1745-2759 1745-2767 |
| language | English |
| last_indexed | 2024-03-08T21:16:11Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj.art-4b28b1422792408c9d9a0a2ed305f5222023-12-21T16:31:35ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672024-12-0119110.1080/17452759.2023.2294336Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloudMengru Liu0Xingwang Bai1Shengxuan Xi2Honghui Dong3Runsheng Li4Haiou Zhang5Xiangman Zhou6School of Mechanical Engineering, University of South China, Hengyang, People’s Republic of ChinaSchool of Mechanical Engineering, University of South China, Hengyang, People’s Republic of ChinaSchool of Mechanical Engineering, University of South China, Hengyang, People’s Republic of ChinaSchool of Mechanical Engineering, University of South China, Hengyang, People’s Republic of ChinaCollege of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao, People’s Republic of ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCollege of Mechanical & Power Engineering of China, China Three Gorges University, Yichang, People’s Republic of ChinaABSTRACTWire and Arc Additive Manufacturing (WAAM) with high efficiency and low-cost is an economical choice for the rapid fabrication of medium-to-large-sized metallic components and has attracted great attention from scholars and entrepreneurs in recent years. However, defects such as porosity, and humps, could occur occasionally after each layer of deposition on weld bead surfaces due to disturbances and process abnormities. Detection and quantitative evaluation of weld bead defects is crucial to ensure successful deposition and the quality of the entire component. In this paper, a novel defect detection and evaluation system was developed for WAAM utilizing machine vision technology. The system incorporated new defect detection algorithms based on analysing the 2D curvature of the weld bead height curve and the 3D curvature of the weld bead point cloud. Furthermore, a defect evaluation algorithm was developed based on reconstructing the normal weld bead contour using geometric features extracted from the accumulated point cloud. This system enables the automatic detection of weld bead morphology during the WAAM process, offering important information about the location, type, and volume of defects for effective interlayer repairs and enhanced part quality.https://www.tandfonline.com/doi/10.1080/17452759.2023.2294336Wire and arc additive manufacturing3D point clouddefect detectionsurface curvature |
| spellingShingle | Mengru Liu Xingwang Bai Shengxuan Xi Honghui Dong Runsheng Li Haiou Zhang Xiangman Zhou Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud Virtual and Physical Prototyping Wire and arc additive manufacturing 3D point cloud defect detection surface curvature |
| title | Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud |
| title_full | Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud |
| title_fullStr | Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud |
| title_full_unstemmed | Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud |
| title_short | Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud |
| title_sort | detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3d point cloud |
| topic | Wire and arc additive manufacturing 3D point cloud defect detection surface curvature |
| url | https://www.tandfonline.com/doi/10.1080/17452759.2023.2294336 |
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