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Detecting Eggshell Cracks by Acoustic Impulse Response and Artificial Neural Networks

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  036170,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.14209) @2003
Authors:   V.K. Jindal, Eakasit Sritham
Keywords:   Acoustic impulse, crack detection, eggshell, neural networks

An experimental system was developed for detecting cracks in eggshells based on acoustic impulse response and artificial neural networks (ANNs). The eggs were lightly impacted eight times individually at equal distance around the equator ring to record the acoustic response of egg surface. The frequency spectra of 4496 acoustic patterns obtained from 300 intact, 52 naturally cracked and 210 artificially cracked eggs were analyzed for training the backpropagation, Kohonen and probabilistic ANNs. Hand candling was used for examining the eggs to verify the presence of surface cracks prior to acoustic testing. Intact eggs produced sound signals mainly exhibiting a single dominant peak in the frequency range of 2500-7000 Hz with signal duration of about 20 ms. The cracked eggs showed frequency spectra in relatively wider frequency range of 550-9000 Hz and shorter signal duration of about 15 ms. In the training of ANNs, 188 neurons were used in the input layer representing signal amplitudes in the frequency range of 597-9173 Hz at 46 Hz interval, and two neurons in the output layer representing either a cracked or intact egg. A backpropagation ANN with three hidden slabs showed best performance with 98.7% crack detection accuracy and 10% false rejects.

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