Deep learning for object recognition and defect analysis in additive manufacturing

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dc.contributor.authorThakur, Chandrbhan
dc.contributor.authorMishra, Santosh Kr.
dc.contributor.authorJose, S. Silva Sajin.
dc.contributor.authorSingh, Purushottam Kumar
dc.contributor.authorUpadhyay, Ram Krishna
dc.date.accessioned2026-10-23T05:17:53Z
dc.date.available2026-10-23T05:17:53Z
dc.date.issued2025-10-15
dc.description.abstractAdditive manufacturing (AM) offers design flexibility and shorter lead times in manufacturing newer products. The layer by layer manufacturing approach often introduces defects that can deteroriate part performance, which require reliable inspection to detect quality. Manual inspection often followed in industries are slow and error prone, which thrusts for the need to automate inspection. This work proposes a modular deep learning framework for objection detection and defect analysis components manufactured by fused deposition modelling. A real-world dataset of 950 images from additively manufactured elevator maintainance components, is taken to test the model and evaluate performance.YOLOv12 and Faster R-CNN are used for object detection, CNN for binary defect classification and MobileNet to classify both defect severity(Levels 0–3) and defect type. The model successfully predicted, whether an object was defective, determined its defect severity level, and correctly labeled the defect type with greater accuracy. YOLOv12 yielded slightly better results in comparison to Faster R-CNN for object detection with a precision of around 93.6% and recall of 95.6%. The model accurately identified whether an object was defective, determined its defect severity level, and correctly labeled the defect type with high precision.
dc.identifier.issn27307727
dc.identifier.urihttps://doi.org/10.1007/s43939-025-00408-2
dc.identifier.urihttp://nitt.ndl.gov.in/handle/123456789/336
dc.language.isoen
dc.publisherDiscover
dc.relation.ispartofseriesDiscover Materials; Vol. 5/ No. 1/ Art. 206
dc.titleDeep learning for object recognition and defect analysis in additive manufacturing
dc.typeArticle
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