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DCM-SAM system improves defect segmentation in metal additive manufacturing

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DCM-SAM, a novel system for segmenting defects in metal additive manufacturing parts using X-ray computed tomography. This system employs a defect-conditioned adaptive mixture of LoRA experts, leveraging a frozen Segment Anything backbone with separate LoRA experts for each defect class. Trained solely on synthetic data, DCM-SAM achieves high accuracy on real scans without direct exposure to them. The system also addresses deployment challenges on AI accelerators, optimizing for efficient execution on Qualcomm Hexagon NPUs. AI

IMPACT This research could lead to more efficient and accurate defect detection in additive manufacturing, potentially improving quality control and reducing waste.

RANK_REASON This is a research paper detailing a new method for defect segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DCM-SAM system improves defect segmentation in metal additive manufacturing

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This is a research paper detailing a new method for defect segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Md Mushfiqur Rahaman, Md Mahedi Hasan, Imtiaz Ahmed, Srinjoy Das ·

    DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation

    arXiv:2609.38811v1 Announce Type: new Abstract: Metal additive manufacturing parts are inspected by X-ray computed tomography, where labelled data is scarce, the pores and inclusions that matter span a few pixels, and inspection must happen at the machine. We present DCM-SAM, a d…

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