Artificial intelligence for identifying impacts on smart composites

Qingshan Shan; Graham King; John Savage

doi:10.1117/12.475254

10 July 2002 Artificial intelligence for identifying impacts on smart composites

Qingshan Shan, Graham King, John Savage

Proceedings Volume 4693, Smart Structures and Materials 2002: Modeling, Signal Processing, and Control; (2002) https://doi.org/10.1117/12.475254
Event: SPIE's 9th Annual International Symposium on Smart Structures and Materials, 2002, San Diego, California, United States

Abstract

This paper present a methodology for impact identification on smart composites. The methodology is composed of four major parts: smart structures for detecting impact to composite; the cross correlation process; feature extraction and adaptive neuro fuzzy inference system (ANFIS) for identifying impacts. The smart structure comprises two piezoelectric transducers embedded in a composite specimen. These are used to measure impact signals caused by foreign object impacts. The impact signals are processed with a cross correlation algorithm and show very clean and meaningful variations in amplitude and shape with differing impact events. Signal features are extracted from the cross correlation results and are processed by methods of mean, standard deviation, kurosis and skewness. The ANFISs are trained, checked, and tested with the feature data to identify abscissas of impact location, ordinates of impact location, and impact magnitude. There are two new aspects to have been developed in this study. The results of implementing the system are discussed and conclusions drawn.

Citation Download Citation

Qingshan Shan, Graham King, and John Savage "Artificial intelligence for identifying impacts on smart composites", Proc. SPIE 4693, Smart Structures and Materials 2002: Modeling, Signal Processing, and Control, (10 July 2002); https://doi.org/10.1117/12.475254

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