Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria, Australia-3122; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria, Australia-3122.
Ultrasonics. 2014 Jan;54(1):312-7. doi: 10.1016/j.ultras.2013.07.018. Epub 2013 Aug 8.
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.
目前，用于防弹衣系统的陶瓷砖采用 X 射线技术进行离线目视检查，这种方法既耗时又非常昂贵。本研究旨在开发一种使用超声传感技术检测、定位和分类反应烧结碳化硅（RSSC）陶瓷砖中各种制造缺陷的方法。诸如游离硅、未烧结碳化硅材料和常规孔隙率等缺陷通常很难使用常规 X 射线照相术检测到。开发了一种替代的检测系统，使用基于人工神经网络（ANN）的信号处理技术检测陶瓷元件中的缺陷。所提出的检测方法侧重于信号的预处理、去噪、小波分解、特征提取以及信号的后处理以进行分类。这项研究有助于开发一种在线检测系统，该系统比现有方法更具成本效益，而且还可以帮助制造商检查高密度区域、缺陷的位置，并实现实时质量控制，包括接受/拒绝标准的实施。
