Neural Network Based System for Nondestructive Testing of Composite Materials Using Low-Frequency Acoustic Methods

摘要

The aim of the research was: the scientific justification and development of non-destructive testing system of products made of composite materials using low-frequency acoustic methods. During the work the following problems have been pointed and solved: 1. Analysis of the statistical characteristics of information signals and forming a set of diagnostic parameters. 2. Justification of necessity to use artificial neural networks for the technical state classification of products from composite materials. Comparative analysis of classification and decision making using of the statistical methods (based on chi-square statistics, metric distances, etc.), separating hyperplanes and neural networks. The type of neural network was defined, as a base for the neural network based classifier of composite materials defects. 3. Hardware and software development of information-diagnostic system for non-destructive testing of products from composite materials. Developed software includes three main parts: mathematical support, dataware and I/O module software. 4. Experimental investigation of developed information-diagnostic system in general.