Advanced non-destructive evaluation of impact damage growth in carbon-fiber-reinforced plastic by electromechanical analysis and machine learning clustering

Abstract In this study, advanced structural health monitoring was conducted on carbon-fiber-reinforced plastic (CFRP) through a non-destructive self-sensing method wherein impact damage growth was tested using the electromechanical properties of the material. The electrical resistance in CFRP composite structures was measured in real time during impact testing. The health state of the structures was monitored in real time during impact energy absorption. Based on the electromechanical data of the CFRP composite structures, k-means clustering and principal component analysis were used to identify the damage types in these structures. Previous self-sensing methods are limited to identifying different damage types, such as delamination, matrix cracking, and fiber breakage. However, the proposed advanced method can identify different damage types in composite structures using only electromechanical behavior. The applicability of the method was verified by using it to assess the impact damage on a three-dimensional wind turbine blade. Thus, this study successfully designed a condition-based monitoring method for analyzing the damage type of CFRP composites and monitoring their current health state, and demonstrated an industry application of the proposed method.