In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing

The evaluation of the geometric conformity of in-layer features in Additive Manufacturing (AM) remains a challenge due to low contrast between the features and the background, textural variations, imaging artifacts, and lighting conditions. This research presents a novel in situ vision-based framework for AM to identify in real-time in-layer features and estimate their shape and printed dimensions and then compare them with the as-processed layer features to evaluate geometrical differences. The framework employs a composite approach to segment features by combining simple thresholding for external features with the Chan–Vese (C–V) active contour model to identify low-contrast internal features. The effect of varying C–V parameters on the segmentation output is also evaluated. The framework was evaluated on a 20.000 mm × 20.000 mm multilayer part with internal features (two circles and a rectangle) printed using Fused Deposition Modeling (FDM). The segmentation performance of the composite method was compared with traditional methods with the results showing the composite method scoring higher in most metrics, including a maximum Jaccard index of 78.34%, effectively segmenting high- and low-contrast features. The improved segmentation enabled the identification of feature geometric differences ranging from 1 to 10 pixels (0.025 mm to 0.250 mm) after printing each layer in situ and in real time. This performance verifies the ability of the framework to detect differences at the pixel level on the evaluation platform. The results demonstrate the potential of the framework to segment features under different contrast and texture conditions, ensure geometric conformity and make decisions on any differences in feature geometry and shape.