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.

翻译：增材制造（AM）的质量控制在汽车、医疗和航空航天等领域的工业应用中至关重要。由收缩和变形引起的几何误差会损害增材制造部件的寿命和性能。此类偏差可通过数字体数据关联（DVC）进行量化，该方法将计算机辅助设计（CAD）模型与所制造部件的X射线计算机断层扫描（XCT）几何结构进行比较。然而，由于缺乏真实变形场或参考变形场，两种模态之间的精确配准具有挑战性。此外，高分辨率XCT体数据的极大尺寸使得计算困难。在本工作中，我们提出了一种基于深度学习的方法，用于估计CAD与XCT体数据之间的体素级变形。我们的方法采用动态分块处理策略来处理高分辨率体数据。除了Dice分数外，我们引入了二值差异图（BDM）来量化二值化CAD与XCT体数据之间的体素级失配，以评估配准精度。与经典DVC方法相比，我们的方法在Dice分数上提升了9.2%，在体素匹配率上提升了9.9%，同时将交互时间从数天缩短至数分钟。这项工作为基于深度学习的DVC方法奠定了基础，以生成补偿网格，进而用于AM生产过程中的闭环关联。此类系统将极大地吸引工业界，因为制造过程将变得更加可靠和高效，从而节省时间和材料。