Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection

TL;DR

This paper introduces a graph-augmented segmentation method using Graph Neural Networks embedded in a U-Net architecture to improve the accuracy and robustness of shape segmentation in laser powder bed fusion inspections under variable conditions.

Contribution

It proposes a novel graph-augmented segmentation approach that preserves global geometric information, enhancing robustness against illumination and layer variability in industrial additive manufacturing.

Findings

Outperforms benchmark techniques in reconstructing lattice structures.

Enhances segmentation consistency under variable lighting conditions.

Demonstrates scalability for industrial in situ inspection.

Abstract

The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image segmentation of powder bed images in Laser Powder Bed Fusion (L-PBF) has been investigated by various authors, leveraging both edge detection and machine learning approaches to identify deviations from nominal geometry. Despite these developments, several challenges remain, including the sensitivity of segmentation performance to industrial illumination conditions and layer-to-layer variability in pixel intensity patterns. The study addresses these limitations by proposing a graph-augmented segmentation approach. The underlying principle consists of preserving the geometrical information at a global level rather than at pixel-wise level, modeling dependencies and relational information among spatial regions with a Graph Neural Network bottleneck embedded into a U-Net architecture. This allows enhancing the consistency and accuracy of the geometry reconstruction in the presence of spatial and layer-wise photometric variability systematically faced in real data. The method is evaluated against benchmark techniques for the in situ reconstruction of lattice structures produced by L-PBF, demonstrating its potential as a scalable solution for robust in situ inspection and geometric verification in industrial environments.