Defect characterisation in laminar composite structures using ultrasonic techniques and artificial neural networks

Abstract

This paper presents an approach to defect detection and characterisation in ultrasonic inspection of laminate composite panels. A set of features coupled with gates was identified along with a method for sub-dividing and thresholding the ultrasonic data, which removes most of the location specific information from the defect data thus increasing the generalisation capabilities of the defect classifier. Validation results obtained from independent defect data indicate that the performance of the presented data description tools coupled with an artificial neural network classifier was able to correctly locate and classify defects at different depths. It was concluded that a structured approach to the pre-processing of ultrasonic testing data, combined with the selective feature extraction for artificial neural network classification, greatly reduces the requirement for artificial neural network training data. Furthermore, it allows for improved performance across a variety of panel geometries.

Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation

“…Furthermore, the conventional ultrasonic inspection process is not automated; thus, the inspections require a lot of time and labor to scan the whole structure. Numerous studies [4,[6][7][8][9][10][11][12][13] have been reported about new techniques and data analyses to overcome these difficulties. For example, ultrasonic arrays [6] have improved the inspection quality and have reduced the inspection costs by performing beam steering with a wide viewing angle through controlled transmission of multiple elements.…”

“…The wavelet-transform has frequently been incorporated with the signal processing to analyze dispersive waves [4,10,11]. Furthermore, some inverse analyses [4,12,13] have been presented to estimate the damage quantitatively using the artificial neural network and genetic algorithm.…”

Data encoding and reconstruction of thermal imaging maps of impact damaged composite Structures using feature space and neural networks

“…Such effects results from impact damage and would change certain features in the testing image of a damaged sample compared to the one before impact. The new captured image will display a pattern which differ from a reference image texture, color or intensity, among others and can be used to establish level and magnitude of damage and component validity as a function of feature distribution change [15][16][17][18][19][20][21][22][23].…”

Correlating and Modeling of Extracted Features from PVT Images of Composites using Optical Flow Technique and Weight Elimination Algorithm Optimization [OFT-WEA]

“…However, if they lie between the hidden layer and the output layer, they can result in outputs beyond the range of the data. Hence, large weights can cause excessive variance of the output, which cause the neural structure to be unstable with values outside the range of the output activation function (Peng et al, 2015;Salazar et al, 2012;Qingsong et al, 2014;Duchi et al, 2011;May et al, 2013;Bahadorinia et al, 2014;Kumar and Harikumar, 2015;Dai et al, 2015;Barry et al, 2016;Ashwini and Yuvaraju, 2016). Weight Elimination mathematical model describes the dynamic changes in Neural Network convergence in relation to error functions.…”