Artificial neural network based algorithm for acoustic impact based nondestructive process monitoring of composite products

Damages like cracks, delaminations, etc., in composite parts have traditionally been evaluated using manual methods like acoustic impact (using measurements in the audio frequencies). This technique is currently used during manufacturing for product quality testing and later for maintenance and assurance of structural integrity. The automation of this technique will significantly improve the reliability of inspection. The signals obtained from the composites are analyzed using signal-processing techniques in the time-frequency domain to build a robust algorithm for detection and identification of defects. A feature vector is constructed using these techniques and then applied to a neural network for defect identification. Comparative studies are conducted to search for the best and most comprehensive feature vector. Results using different signal processing techniques are presented. Similarly comparative results are presented between two different kinds of neural networks (namely Radial Basis functions and MLP) and various architectures in each kind. A low cost data acquisition system has also been developed for acquiring audio signals using the sound card and the microphone in a multi-media PC.