Assisted Defect Recognition (ADR) using AI is revolutionizing digital radiography in aerospace applications, enhancing efficiency, accuracy, and reliability in non-destructive testing (NDT). This presentation will explore the latest advancements in AI-driven ADR, focusing on how machine learning algorithms are improving defect detection in critical aerospace components. Attendees will gain insights into the evolving role of deep learning models in automating flaw identification, reducing human error, and optimizing inspection workflows.

Key aspects of the discussion will include the integration of AI with existing digital radiography systems, the challenges of training models with high-quality datasets, and the balance between automation and expert validation. Case studies will illustrate real-world applications, demonstrating the impact of AI in identifying defects such as porosity, cracks, and inclusions with greater speed and consistency.

By attending this session, industry professionals will better understand how AI can enhance inspection accuracy, reduce costs, and support regulatory compliance. The presentation will also address concerns about the reliability and trustworthiness of AI in NDT, providing strategies for ensuring AI-assisted decision-making aligns with stringent aerospace quality standards. With the rapid advancement of AI technologies, staying informed on these developments is essential for those seeking to improve inspection processes, maintain safety, and drive innovation in aerospace NDT.