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E. Mehrdad

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#edge computing Open access Sep 2026

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.

Himal Sapkota, Prateek Neupane, E. Mehrdad et al. · 0 citations

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