title: Particle Swarm Optimization approach to defect detection in armour ceramics.
abstract: In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function.
authors.affiliation: Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria 3122, Australia; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria 3122, Australia. Electronic address: mkesharaju@swin.edu.au.
authors.forename: Manasa
authors.initials: M
authors.lastname: Kesharaju
authors.affiliation: Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria 3122, Australia; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria 3122, Australia.
authors.forename: Romesh
authors.initials: R
authors.lastname: Nagarajah
keywords: Artificial neural network; Binary Particle Swarm Optimization; Defect detection; Feature selection; SiC ceramics; Ultrasonic testing