AI-based defect detection and self-healing in metal additive manufacturing
This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6Al-4V fabricated using laser-based powder bed fusion. A tailor-made test specimen was designed and manufactured for the nanofocus tube X-ray computed tomography (XCT) system. In situ optical tomography w...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Virtual and Physical Prototyping |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2025.2500671 |
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| author | Jan Akmal Kevin Minet-Lallemand Jukka Kuva Tatu Syvänen Pilvi Ylander Tuomas Puttonen Roy Björkstrand Jouni Partanen Olli Nyrhilä Mika Salmi |
| author_facet | Jan Akmal Kevin Minet-Lallemand Jukka Kuva Tatu Syvänen Pilvi Ylander Tuomas Puttonen Roy Björkstrand Jouni Partanen Olli Nyrhilä Mika Salmi |
| author_sort | Jan Akmal |
| collection | DOAJ |
| description | This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6Al-4V fabricated using laser-based powder bed fusion. A tailor-made test specimen was designed and manufactured for the nanofocus tube X-ray computed tomography (XCT) system. In situ optical tomography was used to capture infrared images containing heat signatures of the hot laser interaction zone. Depicting natural process variation, defective regions were seeded using process manipulation (up to ±30%) in proximity of the experimental standard volumetric energy density (VED). The concomitant defects and heat signatures were both spatially and temporally captured. The results indicate that porosity significantly grows from an average value of 27 parts per million (PPM) to a value of 337 PPM comprising defect sizes of <112 µm when the VED increases by 30%. The outcome confirmed that Ti–6Al–4V can self-heal these defective regions by up to 7 ± 1 layers using the standard VED. A convolutional neural network was trained (n = 211) and was verified with XCT. The model demonstrated prediction accuracy of 94% for the six classes of unfamiliar defective regions. This work enables in-situ detection and healing of defective regions caused by process uncertainty that can shift the quality frontier of novel product design and development. |
| format | Article |
| id | doaj-art-df635c230cb94bccbe78a1f417252995 |
| institution | Kabale University |
| issn | 1745-2759 1745-2767 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj-art-df635c230cb94bccbe78a1f4172529952025-08-20T03:52:57ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672025-12-0120110.1080/17452759.2025.2500671AI-based defect detection and self-healing in metal additive manufacturingJan Akmal0Kevin Minet-Lallemand1Jukka Kuva2Tatu Syvänen3Pilvi Ylander4Tuomas Puttonen5Roy Björkstrand6Jouni Partanen7Olli Nyrhilä8Mika Salmi9Aalto Design Factory, School of Engineering, Aalto University, Espoo, FinlandEOS Metal Materials, Electro Optical Systems Finland Oy, Turku, FinlandGeological Survey of Finland (GTK), Espoo, FinlandEOS Metal Materials, Electro Optical Systems Finland Oy, Turku, FinlandEOS Metal Materials, Electro Optical Systems Finland Oy, Turku, FinlandDepartment of Mechanical Engineering, Aalto University, Espoo, FinlandDepartment of Mechanical Engineering, Aalto University, Espoo, FinlandDepartment of Mechanical Engineering, Aalto University, Espoo, FinlandEOS Metal Materials, Electro Optical Systems Finland Oy, Turku, FinlandDepartment of Mechanical Engineering, Aalto University, Espoo, FinlandThis pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6Al-4V fabricated using laser-based powder bed fusion. A tailor-made test specimen was designed and manufactured for the nanofocus tube X-ray computed tomography (XCT) system. In situ optical tomography was used to capture infrared images containing heat signatures of the hot laser interaction zone. Depicting natural process variation, defective regions were seeded using process manipulation (up to ±30%) in proximity of the experimental standard volumetric energy density (VED). The concomitant defects and heat signatures were both spatially and temporally captured. The results indicate that porosity significantly grows from an average value of 27 parts per million (PPM) to a value of 337 PPM comprising defect sizes of <112 µm when the VED increases by 30%. The outcome confirmed that Ti–6Al–4V can self-heal these defective regions by up to 7 ± 1 layers using the standard VED. A convolutional neural network was trained (n = 211) and was verified with XCT. The model demonstrated prediction accuracy of 94% for the six classes of unfamiliar defective regions. This work enables in-situ detection and healing of defective regions caused by process uncertainty that can shift the quality frontier of novel product design and development.https://www.tandfonline.com/doi/10.1080/17452759.2025.2500671Product design and developmentquality assurancedigital manufacturing3D printingartificial intelligencemachine learning |
| spellingShingle | Jan Akmal Kevin Minet-Lallemand Jukka Kuva Tatu Syvänen Pilvi Ylander Tuomas Puttonen Roy Björkstrand Jouni Partanen Olli Nyrhilä Mika Salmi AI-based defect detection and self-healing in metal additive manufacturing Virtual and Physical Prototyping Product design and development quality assurance digital manufacturing 3D printing artificial intelligence machine learning |
| title | AI-based defect detection and self-healing in metal additive manufacturing |
| title_full | AI-based defect detection and self-healing in metal additive manufacturing |
| title_fullStr | AI-based defect detection and self-healing in metal additive manufacturing |
| title_full_unstemmed | AI-based defect detection and self-healing in metal additive manufacturing |
| title_short | AI-based defect detection and self-healing in metal additive manufacturing |
| title_sort | ai based defect detection and self healing in metal additive manufacturing |
| topic | Product design and development quality assurance digital manufacturing 3D printing artificial intelligence machine learning |
| url | https://www.tandfonline.com/doi/10.1080/17452759.2025.2500671 |
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