en ساخت Manufacturing پژوهشي Research <span style="font-size:10.0pt"><span style="line-height:115%"><span new="" roman="" style="font-family:" times="">In metal casting, detecting defects like pores and cracks in X-ray images is crucial for product quality and safety. This study presents an advanced U-Net architecture for semantic segmentation of defects in the GDXray dataset, achieving superior accuracy. By formulating defect detection as an inverse problem reconstructing material density from X-ray projections the method integrates transfer learning, data augmentation, and Convolutional Block Attention Modules (CBAM) to address low contrast-to-noise ratios and limited data. Pretrained on synthetic Radon transform projections, the U-Net, enhanced with CBAM, sharpens focus on defect regions, improving boundary precision by 5%. Data augmentation, including rotations, flips, and noise injection, generates 5,000 synthetic images to overcome data scarcity. Experiments on 2,727 grayscale GDXray images demonstrate a mean Intersection over :union: (mIoU) of 0.85, a 15% improvement over baseline U-Net models, with 97.8% accuracy for pores and 94.5% for cracks. The inverse problem approach reduces false negatives by 12%, excelling in noisy conditions. Compared to methods like Mask R-CNN, this approach advances non-destructive evaluation (NDE) for casting applications, ensuring reliability and safety. Validated on laboratory X-ray data, the model offers a scalable solution for industrial defect detection. Future work will optimize computational efficiency and explore multi-modal data to enhance robustness.</span></span></span> GDXray Dataset, Defect Detection, Inverse Problems, U-Net, Deep Learning, X-ray Imaging, Casting Defects, Accuracy Improvement, AI in Manufacturing, Industrial NDE 4922 4936 http://ase.iust.ac.ir/browse.php?a_code=A-10-427-5&slc_lang=en&sid=1 Mansour Baghaeian ma.baghaeian@iau.ac.ir 180031947532846005270 180031947532846005270 Yes Assistant Professor, Department of Mechanical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Ehsan Abbasi E.abbasia1380@gmail.com 180031947532846005271 180031947532846005271 No Mechanical Engineering Student, Department of Mechanical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran