Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion
The spatter behavior in the laser powder bed fusion (L-PBF) additive manufacturing is unavoidable owing to the interaction between high-energy laser beam and powder particles. And the droplet spatters generated by the laser induction not only directly reflect the steady-state of the micro-molten poo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785421001691 |
| _version_ | 1819101183083544576 |
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| author | Di Wang Wenhao Dou Yuanhui Ou Yongqiang Yang Chaolin Tan Yingjie Zhang |
| author_facet | Di Wang Wenhao Dou Yuanhui Ou Yongqiang Yang Chaolin Tan Yingjie Zhang |
| author_sort | Di Wang |
| collection | DOAJ |
| description | The spatter behavior in the laser powder bed fusion (L-PBF) additive manufacturing is unavoidable owing to the interaction between high-energy laser beam and powder particles. And the droplet spatters generated by the laser induction not only directly reflect the steady-state of the micro-molten pool but also directly affect the processing quality of the L-PBF process. In order to further understand the mechanism of laser–powder interaction, and the generation and evolution of droplet spatter, the paper presents a droplet spatter feature information extraction algorithm and a manufacturing status classification method. Three kinds of feature information of droplet spatter were collected, extracted, and discussed by high-speed imaging, image processing, and statistical analysis techniques. The evolution mechanisms of the molten pool, the keyhole, and the droplet spatter behavior under different linear energy density inputs were discussed in combination with the single-track build. A mapping model of the droplet spatter behavior characteristics and manufacturing status was processed after that. The accuracy and reliability of the mapping model were verified and analyzed by using the AdaBoost CART classification model. The classification model not only verified the mapping mechanism, and the findings provide a basis and reference for the quality control of the L-PBF process in the future. |
| first_indexed | 2024-12-22T01:14:37Z |
| format | Article |
| id | doaj.art-7ea760519dae4daeaa023603f62303e8 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-22T01:14:37Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-7ea760519dae4daeaa023603f62303e82022-12-21T18:43:53ZengElsevierJournal of Materials Research and Technology2238-78542021-05-011210511064Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusionDi Wang0Wenhao Dou1Yuanhui Ou2Yongqiang Yang3Chaolin Tan4Yingjie Zhang5School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China; Corresponding author.Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, China; Corresponding author.The spatter behavior in the laser powder bed fusion (L-PBF) additive manufacturing is unavoidable owing to the interaction between high-energy laser beam and powder particles. And the droplet spatters generated by the laser induction not only directly reflect the steady-state of the micro-molten pool but also directly affect the processing quality of the L-PBF process. In order to further understand the mechanism of laser–powder interaction, and the generation and evolution of droplet spatter, the paper presents a droplet spatter feature information extraction algorithm and a manufacturing status classification method. Three kinds of feature information of droplet spatter were collected, extracted, and discussed by high-speed imaging, image processing, and statistical analysis techniques. The evolution mechanisms of the molten pool, the keyhole, and the droplet spatter behavior under different linear energy density inputs were discussed in combination with the single-track build. A mapping model of the droplet spatter behavior characteristics and manufacturing status was processed after that. The accuracy and reliability of the mapping model were verified and analyzed by using the AdaBoost CART classification model. The classification model not only verified the mapping mechanism, and the findings provide a basis and reference for the quality control of the L-PBF process in the future.http://www.sciencedirect.com/science/article/pii/S2238785421001691Laser powder bed fusionSpatter behaviorManufacturing statusImage processingStatistical learning method |
| spellingShingle | Di Wang Wenhao Dou Yuanhui Ou Yongqiang Yang Chaolin Tan Yingjie Zhang Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion Journal of Materials Research and Technology Laser powder bed fusion Spatter behavior Manufacturing status Image processing Statistical learning method |
| title | Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion |
| title_full | Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion |
| title_fullStr | Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion |
| title_full_unstemmed | Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion |
| title_short | Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion |
| title_sort | characteristics of droplet spatter behavior and process correlated mapping model in laser powder bed fusion |
| topic | Laser powder bed fusion Spatter behavior Manufacturing status Image processing Statistical learning method |
| url | http://www.sciencedirect.com/science/article/pii/S2238785421001691 |
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