Decision support system for ultrasound inspection of fiber metal laminates using statistical signal processing and neural networks

Abstract

The growth of the aerospace industry has motivated the development of alternative materials. The fiber–metal laminate composites (FML) may replace the monolithic aluminum alloys in aircrafts structure as they present some advantages, such as higher stiffness, lower density and longer lifetime. However, a great variety of deformation modes can lead to failures in these composites and the degradation mechanisms are hard to detect in early stages through regular ultrasonic inspection. This paper aims at the automatic detection of defects (such as fiber fracture and delamination) in fiber–metal laminates composites through ultrasonic testing in the immersion pulse-echo configuration. For this, a neural network based decision support system was designed. The preprocessing stage (feature extraction) comprises Fourier transform and statistical signal processing techniques (Principal Component Analysis and Independent Component Analysis) aiming at extracting discriminant information and reduce redundancy in the set of features. Through the proposed system, classification efficiencies of ∼99% were achieved and the misclassification of signatures corresponding to defects was almost eliminated.