Defect detection in laser powder bed fusion (LPBF) parts is a critical step for in their quality control. Ensuring the integrity of these parts is essential for a broader adoption of this manufacturing process in highly standardized industries such as aerospace. With many challenges to overcome, there is currently no standardized image analysis and segmentation process for the defect analysis of LPBF parts. This process is often manual and operator-dependent, which limits the repeatability and the reproducibility of the analytical methods applied, raising questions about the validity of the analysis. The pore segmentation step is critical for porosity analysis since the pore size and morphology metrics are calculated directly from the results of the segmentation process. In this work, Ti6Al4V specimens with purposely induced and controlled porosity were printed, scanned 5 times on two CT scan systems by two different operators, and then reconstructed as 3D volumes. The porosity in these specimens was analyzed using manual and Otsu thresholding and a convolutional neural network (CNN) deep learning segmentation algorithm. Then, a variance component estimation realized over 75 porosity analyses indicated that, independently of the operator and the CT scan system used, the CNN provided the best repeatability and reproducibility in the LPBF specimens of this study. Finally, a multimodal correlative study using higher resolution laser confocal microscopy observations was used for a multi-scale pore-to-pore comparison and as a reliability assessment of the segmentation algorithms. The validity of the CNN-based pore segmentation was thus assessed through improved repeatability, reproducibility, and reliability.

激光粉末床熔合 (LPBF) 零件中的缺陷检测是质量控制的关键步骤。确保这些零件的完整性对于在航空航天等高度标准化行业中更广泛地采用这种制造工艺至关重要。由于需要克服许多挑战，目前还没有用于 LPBF 零件缺陷分析的标准化图像分析和分割流程。该过程通常是手动且依赖于操作员的，这限制了所应用的分析方法的重复性和再现性，引发了关于分析有效性的问题。孔隙分割步骤对于孔隙率分析至关重要，因为孔径和形态指标是直接根据分割过程的结果计算的。在这项工作中，打印了具有特意诱导和控制孔隙率的 Ti6Al4V 样本，由两名不同的操作员在两个 CT 扫描系统上扫描 5 次，然后重建为 3D 体积。使用手动和 Otsu 阈值以及卷积神经网络 (CNN) 深度学习分割算法分析这些样本的孔隙率。然后，对超过 75 个孔隙率分析实现的方差分量估计表明，独立于操作员和所使用的 CT 扫描系统，CNN 在本研究的 LPBF 样本中提供了最佳的重复性和再现性。最后，使用更高分辨率的激光共焦显微镜观察进行多模态相关研究，用于多尺度孔与孔之间的比较，并作为分割算法的可靠性评估。因此，通过改进的重复性、再现性和可靠性来评估基于 CNN 的孔隙分割的有效性。