The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review. (April 2022)

• Record Type: Journal Article
• Authors: Nelon, Christopher; Myers, Oliver; Hall, Asha
• Is Part Of: Journal of composite materials. Volume 56:Number 9(2022)
• Page Start: 1417
• Page End: 1452
• Publication Date: 2022-04
• Subjects: Damage evaluation -- nondestructive evaluation -- composites -- carbon fiber reinforced polymer -- machine learning -- supervised learning -- artificial neural network -- damage monitoring; Composite materials -- Periodicals; Composites -- Périodiques; 620.118
• DOI: 10.1177/00219983211037048
• Languages: English
• ISSNs: 0021-9983

Abstract

Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.