Defect Detection in Carbon-Fiber Composites using Lamb-Wave Tomographic Methods

Abstract

Lamb-wave tomography (LWT) offers a powerful nondestructive technique for the health assessment of large structures as their propagation properties depend on the thickness and the mechanical properties of the material. Development of a fast and accurate algorithm for defect detection is of paramount importance in any structural-health-monitoring (SHM) system. The present study explores the prospects of LWT as a SHM technique with an accent on developing a suitable algorithm for real-time inspection. Projection data is collected by electronically scanning an array of ultrasonic sensors arranged in a modified cross-hole geometry. The data thus collected is investigated to extract energy profile of the traveling waves. Multiplicative algebraic reconstruction technique (MART) algorithms are used as a tool for tomographic reconstruction from a set of multiple independent measurements. The performance of algorithms is evaluated from the point of view of the cost of algorithm, achievable resolution, and accuracy of results. Experimental results show that MART is capable of characterizing defects in thin isotropic and composite plates within a reasonable error band (±26% normalized, ±2.6 RMS) and is suitable for application to LWT of large structures such as aircraft skins.