Layer-Wise Geometric Deviation Prediction in Metal Additive Manufacturing Using a Geometrically Informed cGAN and X-Ray Computed Tomography
A geometrically informed conditional Generative Adversarial Network (cGAN) is 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.