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English(EN) Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

新的图增强分割技术增强了3D打印的原位检测

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的图增强分割方法，以改进激光粉末床熔融（L-PBF）增材制造中复杂形状的原位检测。该方法利用嵌入在U-Net架构中的图神经网络来保留全局几何信息，克服了对工业照明和层变化敏感的像素级方法的局限性。该技术在重建几何体方面表现出更高的稳定性和准确性，有望实现对L-PBF生产部件的鲁棒工业检测和验证。 AI

影响 提高了工业增材制造检测的几何重建精度。

排序理由 详细介绍增材制造中一种新图像分割方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的图增强分割技术增强了3D打印的原位检测

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Stefano Raimondo (Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy), Matteo Bugatti (Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy), Marco Grasso (Department of Mechanical Engineering, Politecnico di Mi ·

    Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

    arXiv:2604.24234v1 Announce Type: new Abstract: The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image segmentation of powder …

  2. arXiv cs.CV TIER_1 English(EN) · Marco Grasso ·

    Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

    The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image segmentation of powder bed images in Laser Powder Bed Fusion (L-PBF) ha…

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