Layering Defects Detection in Laser Powder Bed Fusion using Embedded Vision System.
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| Title: | Layering Defects Detection in Laser Powder Bed Fusion using Embedded Vision System. |
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| Authors: | Dinh, Duc M.1 dinhducmanh@lqdtu.edu.vn, Muller, Nicolas2 nicolas.muller@ens-paris-saclay.fr, Quinsat, Yann2 yann.quinsat@ens-paris-saclay.fr |
| Source: | Computer-Aided Design & Applications. 2021, Vol. 18 Issue 5, p1111-1118. 8p. |
| Subjects: | Computed tomography, Process capability, Powders, Manufacturing processes, Iron & steel plates |
| Abstract: | Additive Manufacturing (AM) is nowadays emerging as a new way to manufacture complex metallic parts which would be di cult or impossible to obtain using conventional manufacturing processes. However, as with any manufacturing process, monitoring and mastering the process is essential to satisfy the requirements. In AM, most of the defects considered are internal (mainly porosity or inclusions) and due to process capabilities, it is mainly a way to obtain a raw part. This paper focuses on evaluating internal defects in Inconel 718 parts obtained by a laser-based AM technology, i.e. Laser Powder Bed Fusion (LPBF). Some defects can be explained by the interaction between the layering system and the powder bed in the layering stage (keyhole induced by a lack of powder), by non-well melted areas during the melting stage (porosity or keyhole), or by part distortions in the solidification and cooling stages (cracking, warpage, base plate separation,...). In this study, an algorithm is developed to build a 3D Voxel-based model of manufactured parts, based on defects detected on post-layering and post-melting pictures using an embedded visible camera. This model is also compared to a corresponding 3D model of the build that was achieved by X-ray Computed Tomography (CT) scan data, to check its relevance. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer-Aided Design & Applications is the property of Computer-Aided Design & Applications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 171361914 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Layering Defects Detection in Laser Powder Bed Fusion using Embedded Vision System. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dinh%2C+Duc+M%2E%22">Dinh, Duc M.</searchLink><relatesTo>1</relatesTo><i> dinhducmanh@lqdtu.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Muller%2C+Nicolas%22">Muller, Nicolas</searchLink><relatesTo>2</relatesTo><i> nicolas.muller@ens-paris-saclay.fr</i><br /><searchLink fieldCode="AR" term="%22Quinsat%2C+Yann%22">Quinsat, Yann</searchLink><relatesTo>2</relatesTo><i> yann.quinsat@ens-paris-saclay.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer-Aided+Design+%26+Applications%22">Computer-Aided Design & Applications</searchLink>. 2021, Vol. 18 Issue 5, p1111-1118. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Process+capability%22">Process capability</searchLink><br /><searchLink fieldCode="DE" term="%22Powders%22">Powders</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Iron+%26+steel+plates%22">Iron & steel plates</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Additive Manufacturing (AM) is nowadays emerging as a new way to manufacture complex metallic parts which would be di cult or impossible to obtain using conventional manufacturing processes. However, as with any manufacturing process, monitoring and mastering the process is essential to satisfy the requirements. In AM, most of the defects considered are internal (mainly porosity or inclusions) and due to process capabilities, it is mainly a way to obtain a raw part. This paper focuses on evaluating internal defects in Inconel 718 parts obtained by a laser-based AM technology, i.e. Laser Powder Bed Fusion (LPBF). Some defects can be explained by the interaction between the layering system and the powder bed in the layering stage (keyhole induced by a lack of powder), by non-well melted areas during the melting stage (porosity or keyhole), or by part distortions in the solidification and cooling stages (cracking, warpage, base plate separation,...). In this study, an algorithm is developed to build a 3D Voxel-based model of manufactured parts, based on defects detected on post-layering and post-melting pictures using an embedded visible camera. This model is also compared to a corresponding 3D model of the build that was achieved by X-ray Computed Tomography (CT) scan data, to check its relevance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer-Aided Design & Applications is the property of Computer-Aided Design & Applications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.14733/cadaps.2021.1111-1118 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1111 Subjects: – SubjectFull: Computed tomography Type: general – SubjectFull: Process capability Type: general – SubjectFull: Powders Type: general – SubjectFull: Manufacturing processes Type: general – SubjectFull: Iron & steel plates Type: general Titles: – TitleFull: Layering Defects Detection in Laser Powder Bed Fusion using Embedded Vision System. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dinh, Duc M. – PersonEntity: Name: NameFull: Muller, Nicolas – PersonEntity: Name: NameFull: Quinsat, Yann IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 16864360 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Computer-Aided Design & Applications Type: main |
| ResultId | 1 |
