Tips for Effective Machine Learning in NDT/E

Abstract

The proliferation of machine learning (ML) advances will have long-lasting effects on the nondestructive testing/evaluation (NDT/E) community. As these advances impact the field and as new datasets are created to support these methods, it is important for researchers and practitioners to understand the associated challenges. This article provides basic definitions from the ML literature and tips for nondestructive researchers and practitioners to choose an ML architecture and to understand its relationships with the associated data. By the conclusion of this article, the reader will be able to identify the type of ML architecture needed for a given problem, be aware of how characteristics of the data affect the architecture’s training, and understand how to evaluate the ML performance based on properties of the dataset.