Conference
Defects detection and classification in additive manufacturing using deep learning: A comprehensive review.
| Τίτλος: | Defects detection and classification in additive manufacturing using deep learning: A comprehensive review. |
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| Συγγραφείς: | Sarkar, Amlan Kumar, Rabbany, Golam |
| Πηγή: | AIP Conference Proceedings; 2026, Vol. 3419 Issue 1, p1-10, 10p |
| Θεματικοί όροι: | Deep learning, Artificial intelligence, Computer vision, Defect tracking (Computer software development), Machine learning, Solid freeform fabrication, Multisensor data fusion, Quality control |
| Περίληψη: | Production process has been transformed by making complicated geometries possible with less material waste and faster lead times. Although quality assurance continues to face difficulties due to the inherent diversity in additive manufacturing processes and materials. Artificial Intelligence has developed these issues by improving manufacturing quality control through computer vision, machine learning, and multi-sensor data fusion. Therefore, support vector machines improve material categorization, convolutional neural networks improve defects detection, and reinforcement learning improve real-time process optimization. Furthermore, AI applications identify fault localization in photovoltaic systems and surface flaw recognition in steel production to increase the precision of defect detection, streamline production processes and guarantee product quality. Researchers addressed to find out real-world issues such as data preparation, interpretability of models, and smooth integration with current production processes for further development of detecting faults in additive manufacturing. In this research, researchers detected multiple fabric defects by categorizing & classifying them into certain classes by experimenting low data storage requirements & minimal processing times. Therefore, they implemented data cleaning & processing requirements integrated with Gabor Transform for defect classification and achieved 96% accuracy. Researchers also experimented multiple defects identification & classification on automotive industry by applying multiple deep learning approaches. In the future, researchers suggested to work on practical issues finding on this topic including data pretreatment, model interpretability and integration into existed systems. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 194607607 RelevancyScore: 1097 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 1097.41931152344 |
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| Items | – Name: Title Label: Title Group: Ti Data: Defects detection and classification in additive manufacturing using deep learning: A comprehensive review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sarkar%2C+Amlan+Kumar%22">Sarkar, Amlan Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Rabbany%2C+Golam%22">Rabbany, Golam</searchLink> – Name: TitleSource Label: Source Group: Src Data: AIP Conference Proceedings; 2026, Vol. 3419 Issue 1, p1-10, 10p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Solid+freeform+fabrication%22">Solid freeform fabrication</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Production process has been transformed by making complicated geometries possible with less material waste and faster lead times. Although quality assurance continues to face difficulties due to the inherent diversity in additive manufacturing processes and materials. Artificial Intelligence has developed these issues by improving manufacturing quality control through computer vision, machine learning, and multi-sensor data fusion. Therefore, support vector machines improve material categorization, convolutional neural networks improve defects detection, and reinforcement learning improve real-time process optimization. Furthermore, AI applications identify fault localization in photovoltaic systems and surface flaw recognition in steel production to increase the precision of defect detection, streamline production processes and guarantee product quality. Researchers addressed to find out real-world issues such as data preparation, interpretability of models, and smooth integration with current production processes for further development of detecting faults in additive manufacturing. In this research, researchers detected multiple fabric defects by categorizing & classifying them into certain classes by experimenting low data storage requirements & minimal processing times. Therefore, they implemented data cleaning & processing requirements integrated with Gabor Transform for defect classification and achieved 96% accuracy. Researchers also experimented multiple defects identification & classification on automotive industry by applying multiple deep learning approaches. In the future, researchers suggested to work on practical issues finding on this topic including data pretreatment, model interpretability and integration into existed systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics 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: 10.1063/5.0337671 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Solid freeform fabrication Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Quality control Type: general Titles: – TitleFull: Defects detection and classification in additive manufacturing using deep learning: A comprehensive review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sarkar, Amlan Kumar – PersonEntity: Name: NameFull: Rabbany, Golam IsPartOfRelationships: – BibEntity: Dates: – D: 13 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0094243X Numbering: – Type: volume Value: 3419 – Type: issue Value: 1 Titles: – TitleFull: AIP Conference Proceedings Type: main |
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