Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes
Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/4/133 |
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| author | Vignesh Suresh Badrinath Balasubramaniam Li-Hsin Yeh Beiwen Li |
| author_facet | Vignesh Suresh Badrinath Balasubramaniam Li-Hsin Yeh Beiwen Li |
| author_sort | Vignesh Suresh |
| collection | DOAJ |
| description | Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition. |
| format | Article |
| id | doaj-art-8429b485f4e548a39f0fda28b7db3a9a |
| institution | OA Journals |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-8429b485f4e548a39f0fda28b7db3a9a2025-08-20T02:28:36ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-04-019413310.3390/jmmp9040133Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing ProcessesVignesh Suresh0Badrinath Balasubramaniam1Li-Hsin Yeh2Beiwen Li3Alcon Research Laboratories, Fort Worth, TX 76134, USASchool of Electrical and Computer Engineering, University of Georgia, Athens, GA 30605, USADepartment of Mechanical Engineering, Iowa State University, Ames, IA 50011, USASchool of Civil, Environmental and Agricultural Engineering, University of Georgia, Athens, GA 30605, USAAdditive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition.https://www.mdpi.com/2504-4494/9/4/133additive manufacturingthree-dimensional imagingin situ monitoringsurface topographyprocess monitoringdefect detection |
| spellingShingle | Vignesh Suresh Badrinath Balasubramaniam Li-Hsin Yeh Beiwen Li Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes Journal of Manufacturing and Materials Processing additive manufacturing three-dimensional imaging in situ monitoring surface topography process monitoring defect detection |
| title | Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes |
| title_full | Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes |
| title_fullStr | Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes |
| title_full_unstemmed | Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes |
| title_short | Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes |
| title_sort | recent advances in in situ 3d surface topographical monitoring for additive manufacturing processes |
| topic | additive manufacturing three-dimensional imaging in situ monitoring surface topography process monitoring defect detection |
| url | https://www.mdpi.com/2504-4494/9/4/133 |
| work_keys_str_mv | AT vigneshsuresh recentadvancesininsitu3dsurfacetopographicalmonitoringforadditivemanufacturingprocesses AT badrinathbalasubramaniam recentadvancesininsitu3dsurfacetopographicalmonitoringforadditivemanufacturingprocesses AT lihsinyeh recentadvancesininsitu3dsurfacetopographicalmonitoringforadditivemanufacturingprocesses AT beiwenli recentadvancesininsitu3dsurfacetopographicalmonitoringforadditivemanufacturingprocesses |