Visual inspection is one of the most common nondestructive evaluation techniques that is performed through vision. This chapter will summarize visual inspection principles for detecting impact damage in composite materials. Effective parameters and available standards for visual inspection of low-velocity impact damage are introduced, and the relevant challenges are discussed. Current developments in improving visual inspection, including the use of artificial intelligence algorithms, as well as smart coating layers for improved visibility of impact damage are reported. Recent case studies are included, and future research directions are discussed.

Visual inspection is one of the most common non-destructive testing (NDT) methods that offers a fast evaluation of surface damage in aerospace composite structures. However, it is highly dependent on human-related factors and may not detect barely visible impact damage (BVID). In this research, low velocity impact tests with different energy levels are conducted on two groups of composite panels, namely ‘reference’ and ‘sensor-integrated’ samples. Then, the results of impact tests, together with C-scan and visual inspection images, are analysed to define the BVID range and create an original image dataset. Next, four different deep learning models are trained, validated and tested to capture the BVID only from the images of the impacted and non-impacted surfaces. The results show that all four networks can learn and detect BVID quite well, and the sensor-integrated samples reduce the training time and improve the accuracy of deep learning models. ResNet outperforms other networks with the highest accuracy of 96.2% and 98.36% on the back-face of reference and sensor-integrated samples, respectively. The proposed damage recognition method can act as a fast, inexpensive and accurate structural health monitoring tool for composite structures in real-life applications.

This paper investigates the fatigue behaviour of pseudo-ductile Quasi-Isotropic (QI) interlayer hybrids with un-notched and open-hole configurations. Two different types of QI pseudo-ductile hybrids were evaluated; HighC, with carbon to glass thickness ratio of 0.29, that is made of thin-ply M46JB-carbon/epoxy and thin-ply Xstrand-glass/epoxy prepregs, and LowC, with carbon to glass thickness ratio of 0.19, that is made of thin-ply T300-carbon/epoxy and standard-ply S-glass/epoxy prepregs. The hybrid configurations were loaded at 4 Hz in tension–tension fatigue without any initial damage and at different percentages of the pseudo-yield stress (σ_py) at which damage initiates. It was observed that there is no stiffness reduction, after 100,000 cycles, for a stress level of 80 % and 50 % of the σ_py for the un-notched and open-hole laminates, respectively. By increasing the stress level to 90 % and 70 % of the σ_py for the un-notched and open-hole laminates, respectively, there is a gradual stiffness reduction due to the appearance of matrix cracking and delamination in LowC, but no gradual reduction and no visible damage were observed for HighC. The final failure is more brittle and happens at a lower number of cycles for HighC compared with LowC. Different damage extents were observed for the investigated laminates before the final sudden failure due to variables such as the ply thickness, the cyclic energy release rate and the interfacial fracture toughness.

Recently emerging mechanochromic systems are becoming highly attractive for structural health monitoring (SHM) purposes in various industries, such as civil, wind, and aerospace, to improve the safety and performance of structures. These are based on self-reporting polymer composites which provide a light-weight sensor with an easy-to-read visual cue for SHM purposes. The present paper reports a critical overview of mechanochromic self-reporting approaches and discusses the outlook for future development in the field. Design principles and cutting-edge applications of the main physical- and chemical-based self-reporting mechanisms, i.e., mechanochromism based on dye-filled materials, modified polymers, structural color materials, and smart hybrid composite sensors, are presented with special attention to SHM. These emerging sensors create a new generation of user-friendly, cheap, and power-free SHM systems, guaranteeing economic and technological advantages that will open up new horizons for innovative, safer, and lighter composite products with significantly lower maintenance costs.