Non-Contact Detection of Delamination in Composite Laminates Coated with a Mechanoluminescent Sensor Using Convolutional AutoEncoder

Abstract

Delamination is a typical defect of carbon fiber-reinforced composite laminates. Detecting delamination is very important in the performance of laminated composite structures. Structural Health Monitoring (SHM) methods using the latest sensors have been proposed to detect delamination that occurs during the operation of laminated composite structures. However, most sensors used in SHM methods measure data in the contact form and do not provide visual information about delamination. Research into mechanoluminescent sensors (ML) that can address the limitations of existing sensors has been actively conducted for decades. The ML sensor responds to mechanical deformation and emits light proportional to mechanical stimuli, thanks it can provide visual information about changes in the physical quantity of the entire structure. Many researchers focus on detecting cracks in structures and impact damage with the ML sensor. This paper presents a method of detecting the delamination of composites using ML sensors. A Convolutional AutoEncoder (CAE) was used to automatically extract the delamination positions from light emission images, which offers better performance compared to edge detection methods.