Review of machine learning applications for defect detection in composite materials

Abstract

Machine learning (ML) techniques have shown promising applications in a broad range of topics in engineering, composite materials behavior analysis, and manufacturing. This paper reviews successful ML implementations for defect and damage identification and progression in composites. The focus is on predicting composites' responses under specific loads and environments and optimizing setting and imperfection sensitivity. Discussions and recommendations toward promising ML implementation practices for fruitful interpretable results in the composites’ analysis are provided.