Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing
The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issue...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400102 |
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| author | Abdul Rahman Sani Ali Zolfagharian Abbas Z. Kouzani |
| author_facet | Abdul Rahman Sani Ali Zolfagharian Abbas Z. Kouzani |
| author_sort | Abdul Rahman Sani |
| collection | DOAJ |
| description | The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials. |
| format | Article |
| id | doaj-art-9efbc07484664c7ba5b88df06153e501 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-9efbc07484664c7ba5b88df06153e5012025-08-20T01:47:33ZengWileyAdvanced Intelligent Systems2640-45672024-10-01610n/an/a10.1002/aisy.202400102Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D PrintingAbdul Rahman Sani0Ali Zolfagharian1Abbas Z. Kouzani2School of Engineering Deakin University Geelong Victoria 3216 AustraliaSchool of Engineering Deakin University Geelong Victoria 3216 AustraliaSchool of Engineering Deakin University Geelong Victoria 3216 AustraliaThe advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.https://doi.org/10.1002/aisy.202400102additive manufacturingartificial intelligenceAI-augmented additive manufacturing3D Printing4D printing |
| spellingShingle | Abdul Rahman Sani Ali Zolfagharian Abbas Z. Kouzani Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing Advanced Intelligent Systems additive manufacturing artificial intelligence AI-augmented additive manufacturing 3D Printing 4D printing |
| title | Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing |
| title_full | Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing |
| title_fullStr | Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing |
| title_full_unstemmed | Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing |
| title_short | Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing |
| title_sort | artificial intelligence augmented additive manufacturing insights on closed loop 3d printing |
| topic | additive manufacturing artificial intelligence AI-augmented additive manufacturing 3D Printing 4D printing |
| url | https://doi.org/10.1002/aisy.202400102 |
| work_keys_str_mv | AT abdulrahmansani artificialintelligenceaugmentedadditivemanufacturinginsightsonclosedloop3dprinting AT alizolfagharian artificialintelligenceaugmentedadditivemanufacturinginsightsonclosedloop3dprinting AT abbaszkouzani artificialintelligenceaugmentedadditivemanufacturinginsightsonclosedloop3dprinting |