Health monitoring in composite laminates through impregnated smart sensors

• Main Author: Tai, Daryl
• Other Authors: Sridhar Idapalapati
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering
• Online Access: https://hdl.handle.net/10356/176504
• Institution: Nanyang Technological University

Structural health monitoring (SHM) is a promising approach for aircraft structural damage detection due to its efficiency. It has potential for reducing aircraft maintenance costs through condition-based maintenance and automatic damage detection (no human labor required). However, it is still a field in development and there are many obstacles to be overcome before they can be deployed for commercial uses. Piezoceramic sensors are commonly used as part of the sensor network in SHM and a fundamental issue with it is the consistency of the signals produced. Specifically, sensors produce significantly different voltage levels in response to an impact equidistant from them. This study investigated the effects of 3 factors on signal consistency: material type, distance of sensor from impact surface, sensor density. An experiment via computer simulation was also conducted to investigate the effect of the sensor network’s geometric arrangement on expected number of sensors within impact range. Experimental results show that changing the laminate material from GFRP M21 E-glass to CFRP 8552 yielded approximately 75% reduction in the coefficient of variation (CoV) of the signals produced. Increasing the depth of embedment from 1.55mm to 4.53mm in GFRP M21 E-glass reduced signal CoV by 86.3%. Increasing sensor density from 4 sensors (in a 180mm-by-180mm laminate area) to 6 reduces CoV by 24.0%. However, it is found that the geometric arrangement of a sensor network has no effect on the expected number of sensors within impact range. This study concluded that using stiffer materials (either through changing material type or embedding sensors at deeper levels) and using a large number of sensors can improve signal consistency.