Machine learning and NDE: Past, present, and future

Abstract

Recent high-profile successes in machine learning have found solutions to problems that were long-thought to be decades away and has generated renewed interest in artificial intelligence (AT) and machine learning (ML) research. For example, the recent success and rapid commercialization of deep learning has catapulted technical achievements in many fields, including computer vision, speech recognition, games, and machine translation. These successes present new opportunities for advancing nondestructive evaluation (NDE) techniques. This paper reviews the fundamental concepts of ML and their underlying connections to statistics. We then discuss past applications and methods of ML for NDE. We also describe the still unresolved challenges for this field, such as the unavailability of reliable training data. We then discuss current research in ML for NDE that is directed toward solving these challenges. Finally, we outline how the most recent advances in ML, such as deep learning and transfer learning, have the potential to revolutionize how we design future NDE solutions.