Damage detection in composites using non-destructive testing aided by ANN technique: A review

Damages are inevitable in structures and effective damage detection techniques are important for maintaining their health. Many weight-sensitive engineering applications use composite materials, especially fiber-reinforced laminates. Common damages of these materials include delamination, fiber breakage, fiber pull-out, etc. Various non-destructive testing (NDT) techniques are reported in the literature for damage detection in composites, such as ultrasonic testing, vibration-based techniques, acoustic emission technique, optical NDT and imagining techniques. However, due to the complex properties of composite materials, conventional techniques for analyzing NDT data are difficult to implement. In this context, artificial neural network (ANN) technique is a promising alternative for analyzing NDT data for damage detection. In this study, an attempt is made to explore the state-of-the-art of damage detection in composites using NDT aided by ANN. The work discusses the pros and cons of different methods and is expected to help in identifying the appropriate method for damage detection in composites.

Automated summary

Damages in structures are inevitable and it is important to have effective damage detection techniques. Many engineering applications use composite materials, and common damages include delamination, fiber breakage, and fiber pull-out. Various non-destructive testing techniques have been reported for damage detection in composites, but traditional techniques are difficult to implement due to the complex properties of the materials. This study explores the use of artificial neural networks for analyzing NDT data for damage detection and discusses the pros and cons of different methods.