English(EN) DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation

DCM-SAM 系统改进了金属增材制造中的缺陷分割

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了 DCM-SAM，这是一种用于通过 X 射线计算机断层扫描分割金属增材制造零件中缺陷的新型系统。该系统采用缺陷条件自适应 LoRA 专家混合模型，利用固定的 Segment Anything 主干，并为每个缺陷类别配备单独的 LoRA 专家。DCM-SAM 仅使用合成数据进行训练，就能在未直接接触真实扫描的情况下实现高精度。该系统还解决了在 AI 加速器上部署的挑战，针对 Qualcomm Hexagon NPU 进行了优化，以实现高效执行。 AI

影响这项研究可能带来更高效、更准确的增材制造缺陷检测，从而提高质量控制并减少浪费。

排序理由这是一篇详细介绍一种新的缺陷分割方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Md Mushfiqur Rahaman, Md Mahedi Hasan, Imtiaz Ahmed, Srinjoy Das · 2026-10-01 04:00

DCM-SAM: 用于NPU部署的AM缺陷分割的缺陷条件LoRA专家混合模型

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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报道来源 [1]

DCM-SAM: 用于NPU部署的AM缺陷分割的缺陷条件LoRA专家混合模型

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