Applying Machine Learning to Automate Interpretation of Ultrasonic Non-Destructive Evaluation Data
Explore a comprehensive lecture on applying machine learning techniques to automate the interpretation of ultrasonic Non-Destructive Evaluation (NDE) data. Delve into the critical role of NDE in ensuring the safe manufacture and operation of safety-critical engineering components. Discover how data-driven artificial intelligence and deep machine learning are revolutionizing various industries, and learn why the NDE field is currently lagging behind in adopting these technologies. Examine ultrasonic examples demonstrating the application of machine learning to three fundamental NDE activities: property measurement, defect characterization, and defect detection. Understand the importance of large-scale, high-fidelity training datasets and scalable simulation tools in enabling machine learning algorithms for NDE. Analyze the benefits and potential pitfalls of applying machine learning to NDE data, and gain insights into future research directions in this field. This 55-minute talk by Paul Wilcox at the Alan Turing Institute provides valuable knowledge for professionals and researchers interested in the intersection of machine learning and non-destructive evaluation techniques. Read more