Additive manufacturing (AM) enables the production of complex geometries but presents challenges related to defect formation, especially in powder bed fusion technologies where defects like porosities are common. Efficient and accurate defect identification in metallic parts is critical but often time-consuming, error-prone, and costly. X-ray computed tomography (XCT) is widely used for non-destructive defect detection. Still, it suffers from high computational demands, numerical artifacts, and reduced spatial resolution due to the reconstruction process. Deep learning defect inspections require large dataset but only small dataset are available, as of now. To address this challenge, a previously published method introduced a 2D defect extractor operating directly in the Radon space. While effective, this approach is limited by its slice-by-slice processing. To overcome this, we propose the Sinogram Defect Augmentation Methodology (SinoDAM), which refines the previous workflow into a 3D approach. SinoDAM generates volumetric variations commonly observed in industrial conditions by incorporating geometric transformations. Operating directly in the sinogram domain ensures the physical plausibility of synthetic data while avoiding inconsistencies introduced during reconstruction. SinoDAM offers an effective solution for synthesizing diverse and realistic dataset in the Radon space. It is designed to support future deep learning applications in defect detection and classification, offering a more scalable and accurate solution for quality control in AM.