NON-DESTRUCTIVE INCLUSION DETECTION AND QUANTIFICATION FOR CARBON FIBER LAMINATED COMPOSITES WITH PULSE-ECHO ULTRASONICS

Woven carbon fiber composites have desirable properties for many applications due to their high strength to weight ratio and thermal dimensionality, however many classical methods for inspection are not sufficient for damage detection and quantification. Due to the banded nature of the composite lamina, defects often occur between lamina and are not identifiable by standard visual methods. This may result in unexpected or premature failure. Similarly, assuming a part is damaged and overestimating the damage zone may result in parts being replaced unnecessarily. In this study a focused ultrasound transducer was used to detect a 3 micron thick sheet of Polytetrafluoroethylene (PTFE) placed between carbon fiber lamina. The scans were performed in a grid pattern, also known as a c-scan. Features in the time domain and the frequency domain are extracted for classification, as well as a newly developed block correlation method. The results presented in the paper highlight the various features and a discussion is presented of the advantages and disadvantages of the various techniques implemented to identify the defect. Background and Requirements Carbon fiber reinforced composites (CFRC) have been used in the aerospace industry for many years because of their desirable properties. In recent years they have also been used in various applications in the automotive industry and their usage is rapidly increasing (see e.g., [1]). Laminated composites offer particular challenges in quality control, damage evaluation/repair and inspection because many issues that occur at the interface between individual lamina cannot be seen from the surface. Also in certain applications features are purposefully [2] and inadvertently [3] inserted between carbon fiber layers that are neither carbon fiber nor resin. These features are termed foreign objects (FO) in the present paper. A variety of nondestructive testing techniques have been developed for inspection including pulse thermography, x-ray, MRI, ultrasound and others. Ultrasound in many applications is useful due to its portability, versatility, and relatively low infrastructure costs (see e.g., [4]). The outcome of an ultrasonic scan is a compilation of individual a-scans forming a complete c-scan for later analysis (see e.g., [4]). An a-scan represents the received acoustic wave from a single pulse of the transducer at a single location on a part. The c-scan is classically identified as some form of the signal intensity at a specific depth within the part for all of the a-scans across the part. The c-scan is often interpreted in a spatial x-y surface plot with contours of signal intensity corresponding to a specific depth within the part. There are a series of papers that use Artificial Neural Networks (ANNs) (see e.g., [5]–[8]) to aid in defect characterization. Recently work in the use of tensor based analysis methods incorporating all of the a-scan signal [9] and statistical Bayesian methods [10] have also shown promise. ANNs have been used for signal classification of the resulting waveform of an ultrasonic ascan in a variety of studies. Several authors have used ANNs to analyze features of CFRCs.