Layer-Wise Geometric Deviation Prediction in Metal Additive Manufacturing Using a Geometrically Informed cGAN and X-Ray Computed Tomography

Geometric deviations in unsupported overhang features pose one of the most persistent quality challenges in Laser Powder Bed Fusion (LPBF), where even small deviations from the intended geometry can undermine part functionality and reliability. This study presents a geometrically informed conditional Generative Adversarial Network (cGAN), implemented through the Pix2Pix framework, to predict layer-wise geometric deviations in LPBF-printed parts with overhang geometries, using paired two-dimensional Computer-Aided Design (2D CAD) slices and corresponding X-ray Computed Tomography (XCT)-derived ground truth slices. The study investigates how geometric information can be encoded within the conditional input of the Pix2Pix framework to more effectively guide deviation prediction. A total of 18 models were trained and evaluated across multiple overhang geometry groups and batch size configurations, assessed through a combination of perceptual, structural, and boundary-focused metrics, namely Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), and Edge Intersection over Union (Edge IoU). The results demonstrated that color-coded inputs consistently improved prediction fidelity, perceptual similarity, and edge alignment relative to their non-color-coded counterparts. Furthermore, a model trained on a balanced multi-geometry dataset showed improved prediction performance on withheld 30° and 60° overhang configurations within the benchmark geometry family. The proposed framework offers a data-driven, design-stage tool for anticipating geometry-dependent deviations in LPBF overhang structures, supporting design for additive manufacturing.