Electromagnetic nondestructive testing, especially eddy current testing (ECT), has been extensively used to detect and evaluate damage such as surface defects in materials. Recently, ECT has attracted attention as a method for nondestructively analyzing the microstructures and properties of materials based on the fact that the obtained signal is dependent on the material properties and the presence of defects. Therefore, this review focuses on research mainly conducted by our group on nondestructive testing using eddy currents.

This paper focuses on embedded fiber-optic sensors used in smart composites and presents some previous academic research. It summarizes the disadvantages of embedding, the methods used to solve them, and the advantages when applied to advanced composites. Advanced composites are susceptible to the influence of molding methods, which also relates to the diagnosis of structural integrity during subsequent operation. Embedded fiber-optic sensors are expected to contribute to the life cycle monitoring of advanced composite structures and the optimization of new molding methods.

High-strength and functional carbon-fiber-reinforced polymer (CFRP) composite materials with piezoelectric properties are essential for the development of science and technology for multifunctional structures. This review focuses on carbon-fiber-reinforced polymer piezoelectric composites that convert kinetic energy into electrical energy. First, a general description of the principles of piezoelectric composites is presented, and the development status of piezoelectric laminated CFRP composite materials for application in CFRP damage detection and energy harvesting are introduced. Subsequently, the fabrication methods of piezoelectric particles dispersed CFRP composite materials and research guidelines for evaluating the piezoelectric, sensing, and energy harvesting characteristics are presented, and the results are summarized.

This paper focuses on non-destructive inspection methods of carbon fiber reinforced plastics (CFRPs) using infrared thermography. Infrared thermographic non-destructive inspection can be generally classified into two methods: passive and active thermography. In passive thermography, temperature changes caused by natural environmental conditions (e.g., solar radiation or temperature variation between daytime and night) are observed using an infrared camera. In contrast, an inspection object is artificially heated in active thermography, and temperature variation is monitored during or after the heating. In active thermography, many inspection methods differentiated by heating or the post-processing methods of the observed temperature data have been reported. This paper deals with several active thermography inspection methods (e.g., pulse thermography, pulse phase thermography using phase images obtained by Fourier transformation, laser scanning heating thermography, and ultrasound-excited thermography) and provides their results.

Machine learning (ML), which includes deep learning, has been applied to structural design and in understanding the behavior of materials. ML has self-learning capabilities, reduces the computer processing time with large datasets, and can obtain highly accurate results. However, because ML is a data-driven method, the quantity and quality of data significantly affect the accuracy of ML, and therefore properly designed AI algorithms and virtual reality models are necessary. Continued research efforts in this area are thus required. This article presents the latest applications of ML for composite materials.