Formnext – Hub for Additive Manufacturing

Even when Formnext isn’t on in Frankfurt, we’re the partner you can always rely on for information and services relating to AM 365 days a year. Now, you can also explore the AM hub on our new website.


Expert Insight

Deep Learning-based XCT for Accelerated Non-Destructive Evaluation in Metal Additive Manufacturing

Non-destructive characterization and evaluation (NDC/NDE) using X-ray computed tomography (XCT) is crucial in metal additive manufacturing (AM) for the qualification and certification of manufactured parts. Despite its importance, the beam hardening effect and resource-intensive nature of XCT have limited its role in high-throughput large scale NDE in metal AM.

This work presents fast, deep learning-based algorithms developed under the Simurgh framework, designed to optimize XCT for scalable characterization in metal AM. Unlike existing approaches, our algorithms uniquely leverage CAD models of the parts, physics-based information, and both supervised and unsupervised deep learning networks, including generative adversarial networks (GANs). This combination enhances the quality of the reconstruction while significantly reducing scan time. Our algorithms have shown considerable improvements in NDE for metal AM, enabling faster scans, lower costs, and labor, while providing higher-quality results without imposing additional  computational burdens. We present results demonstrating how these approaches enhance defect detection capabilities and reduce acquisition, reconstruction, and analysis time and complexity for components made of both low- and high-density metallic materials. The application of these algorithms allows for high-throughput characterization of hundreds of coupons in a matter of weeks, offering a comprehensive solution for faster, more accurate, and cost-effective industrial CT imaging. 

The speaker is:
Dr. Amir Koushyar Ziabari, R&D Staff Scientist, Oak Ridge National Lab


  • Design and product development