用深度学习提升3D打印件质量检测精度，大幅缩短计算时间。
Deep Learning based 3D Volume Correlation for Additive Manufacturing Using High-Resolution Industrial X-ray Computed Tomography
- 采用动态块处理策略，解决高分辨率工业CT数据计算难题。
- 相比传统方法，分割准确率提升9.2%，体素匹配率提高9.9%。
- 适合需要闭环质量控制的航空航天、医疗等高端制造领域。
增材制造（AM）的质量控制在汽车、医疗和航空航天等领域至关重要。由收缩和变形引起的几何偏差可能影响制造件的寿命与性能。数字体积相关（DVC）技术通过对比计算机辅助设计（CAD）模型与实际部件的X射线断层扫描（XCT）几何结构来量化这些偏差。然而，由于缺乏真实变形场作为参考，两种模态间的精确配准极具挑战性；同时，高分辨率XCT数据体积庞大，导致计算困难。本文提出一种基于深度学习的体素级变形估计方法，采用动态块处理策略应对高分辨率数据。除使用Dice分数外，还引入二值差异图（BDM）量化二值化后的CAD与XCT体积间的体素级不匹配情况。实验表明，本方法在Dice分数上比经典DVC提升9.2%，体素匹配率提高9.9%，并将交互时间从数天缩短至分钟级。该工作为基于深度学习的DVC方法生成补偿网格奠定了基础，未来可用于增材制造过程中的闭环相关分析，显著提升生产可靠性与效率，节省时间和材料。
原文摘要 · Abstract (English)
Quality control in additive manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the computer-aided design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice Score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2\% improvement in the Dice Score and a 9.9\% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industries since the manufacturing process will become more reliable and efficient, saving time and material.
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