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

• Published in: Composites science and technology 2022-02, Vol.218, p.109094, Article 109094
• Main Authors: Lee, In Yong, Roh, Hyung Doh, Park, Hyung Wook, Park, Young-Bin
• Format: Article
• Language: English
• Subjects: Breakage; Carbon fiber reinforced plastics; Carbon fibers; Cluster analysis; Clustering; Composite structures; Condition monitoring; Cracking (fracturing); Damage assessment; Damage detection; Energy absorption; Fiber reinforced composites; Fiber reinforced plastics; Impact damage; Industrial applications; Machine learning; Monitoring systems; Non-destructive testing; Nondestructive testing; Polymer-matrix composites; Principal components analysis; Real time; Smart materials; Structural health monitoring; Turbine blades; Vector quantization; Wind damage; Wind turbines
• ISSN: 0266-3538; 1879-1050
• DOI: 10.1016/j.compscitech.2021.109094

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. Schematic of overall real time advanced real-time non-destructive evaluation methodology in impact damage growth testing using its electromechanical behavior. [Display omitted]