title: Ultrasonic sensor based defect detection and characterisation of ceramics.
abstract: 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.
authors: Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria, Australia-3122; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria, Australia-3122. Electronic address: mkesharaju@swin.edu.au.
authors: Manasa
authors: M
authors: Kesharaju
authors: Romesh
authors: R
authors: Nagarajah
authors: Tonzhua
authors: T
authors: Zhang
authors: Ian
authors: I
authors: Crouch
mesh_terms: D000465:Algorithms; D017554:Carbon Compounds, Inorganic / Q000032:analysis / Q000737:chemistry*; D002516:Ceramics / Q000032:analysis / Q000737:chemistry*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D008422:Materials Testing / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D017655:Silicon Compounds / Q000032:analysis / Q000737:chemistry*; D014463:Ultrasonography / Q000295:instrumentation* / Q000379:methods*; D058067:Wavelet Analysis
chemical_list: D017554:Carbon Compounds, Inorganic; D017655:Silicon Compounds; C022088:silicon carbide
keywords: Defect classification; Linear discriminant analysis; Neural networks; Ultrasonic testing; Wavelet transform