Deep learning-based image segmentation for defect detection in additive manufacturing: an overview
Problem
Additive manufacturing parts can contain defects such as porosity, melt-pool anomalies and spattering, blocking reliable use in medical, aerospace and automotive applications.
Why it matters
Flawless parts are critical for safety-critical adoption of additive manufacturing in these industries.
What the AI did
The paper reviews computer vision-based deep learning methods that localize and segment layer-wise defects in situ during additive manufacturing processes.
Results
not stated in source
Impact
The overview maps deep learning methods for in situ defect localization toward error-free parts; no measured results stated.
Record
| Venue | The International Journal of Advanced Manufacturing Technology |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Sourabh Deshpande, Vysakh Venugopal, Manish Kumar, Sam Anand |
| Citations | 53 (OpenAlex, at import time) |
| References | 97 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s00170-024-14191-6 |
| Open access | hybrid |
| Keywords | deep learning, image segmentation, defect detection, additive manufacturing, computer vision |
Importance rating
significant — thousands to millions affected
Defects block reliable additive manufacturing adoption in medical, aerospace, and automotive safety-critical uses; significant multi-industry problem, but no scale quantified and paper is a review.
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