Ceramic tiles, used in body armour

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique
that is both time consuming and very expensive. The aim of this research is to develop a methodology
to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide
(RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon
carbide material and conventional porosity are often difficult to detect using conventional X-radiography.
An alternative inspection system was developed to detect defects in ceramic components using an
Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed
focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and
post-processing of the signals for classification purposes. This research contributes to developing an
on-line inspection system that would be far more cost effective than present methods and, moreover,
assist manufacturers in checking the location of high density areas, defects and enable real time quality
control, including the implementation of accept/reject criteria.