Artificial intelligence for materials damage diagnostics and prognostics

The reliable detection and evaluation of damage in materials represents a major engineering concern to prevent component failure in a variety of applications. Defects appearing during the manufacturing stage or during the life of a component due to service loads cause wear and a reduction in overall usefulness. To avoid such unexpected failure of materials, artificial intelligence (AI) can augment human expertise to characterize, monitor, assess, and test for damage in materials. In this context, this chapter provides an overview of AI methods that have been used to perform material damage diagnostics, as well as of methods that use such information to forecast material behavior in their service environment, i.e., for prognostics. The respective advantages and disadvantages of each method are also discussed. The chapter concludes with remarks related to the outlook of using AI methods for material damage in manufacturing and addresses current and future related challenges and opportunities.