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About

Shaun McKnight is a leading researcher at the forefront of Non-Destructive Evaluation (NDE) 4.0, specializing in the automation and intelligent analysis of inspection data for critical infrastructure. His work primarily focuses on advancing eddy current and ultrasonic phased array testing, with a strong emphasis on integrating machine learning and human-machine collaboration. McKnight’s major contributions include pioneering automated real-time eddy current array inspection for nuclear assets, a method that overcomes the limitations of traditional volumetric techniques in detecting surface-breaking defects. His research on applying machine learning object detection to phased array ultrasonic testing of Carbon Fibre Reinforced Plastics (CFRPs) has been highly influential, with his 2024 paper garnering 25 citations for its potential to revolutionize aerospace quality assurance. Most recently, McKnight has been a key voice in defining NDE 4.0 strategies, proposing collaborative automation frameworks that bridge the gap between advanced AI and human expertise. With his most-cited work (31 citations) addressing a critical safety need in the nuclear sector, McKnight is shaping the future of intelligent, automated inspection for high-stakes industries.