In-situ layerwise imaging in laser powder bed fusion (L-PBF) has been implemented by many system developers to monitor the powder bed homogeneity. Increasing attention has been recently devoted to the possibility of using the same sensing approach to detect also in-plane and out-of-plane geometrical distortions of the part while it is being produced. To this aim, seminal works investigated the suitability of various image segmentation algorithms and assessed the accuracy of layerwise dimensional and geometrical measurements. Nevertheless, there is a lack of automated methods to identify, in-situ and in-process, geometrical defects and out-of-control deviations from the nominal geometry. This study presents a methodology that combines an active contours methodology for image segmentation with a statistical process monitoring approach suitable to deal with complex geometries that change layer by layer. The proposed approach enables a data-driven and automated alarm rule to detect the onset of geometrical distortions during the build by comparing the slice contour reconstruction with the nominal geometry in each layer. Moreover, by coupling edge-based and region-based segmentation techniques, the method is sufficiently robust to be applied to imaging and illumination setups that are already available on industrial L-PBF systems. The effectiveness of the proposed approach was tested on a real case study involving the L-PBF of complex Ti6Al4V parts that exhibited local geometrical distortions.

许多系统开发人员已经实施了激光粉末床融合（L-PBF）中的原位分层成像，以监视粉末床的均匀性。近来，人们越来越关注使用相同的传感方法来检测零件在制造时的平面内和平面外几何变形的可能性。为此，开创性的作品研究了各种图像分割算法的适用性，并评估了分层尺寸和几何测量的准确性。然而，缺乏自动方法来就地和过程中识别几何缺陷和与标称几何形状的失控偏差。这项研究提出了一种方法，该方法结合了用于图像分割的主动轮廓方法和适合处理逐层变化的复杂几何形状的统计过程监视方法。通过将切片轮廓重建与每层中的标称几何进行比较，所提出的方法使数据驱动的自动警报规则能够在构建过程中检测几何变形的开始。此外，通过结合基于边缘和基于区域的分割技术，该方法具有足够的鲁棒性，可应用于工业L-PBF系统上已经可用的成像和照明设置。在涉及包含局部几何变形的复杂Ti6Al4V零件的L-PBF的实际案例研究中测试了该方法的有效性。