e-ISSN:0976-5166
p-ISSN:2231-3850


INDIAN JOURNAL OF COMPUTER SCIENCE AND ENGINEERING

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ABSTRACT

Title : ARTIFICIAL NEURAL NETWORK BASED CLASSIFICATION OF AUSTENITIC STAINLESS STEEL WELD DEFECTS IN TOFD TECHNIQUE
Authors : S.Lalithakumari, Dr.B.Sheelarani, Dr.B.Venkatraman
Keywords : BFGS quasi-Newton back propagation., time scale features, classification accuracy, performance function
Issue Date : Dec 2011-Jan 2012
Abstract :
In this paper, an automatic detection system to recognize welding defects based on Time of flight diffraction technique is described. The proposed classification consists in detecting the four types of austenitic stainless steel weld defects and non-defect type. The austenitic stainless steel welds with artificially created defects have been considered. A scan Signals are obtained by conducting TOFD experiment on these weld defects. To improve the efficiency of defect detection, a discrete wavelet transform based denoising was also adopted as a preprocessing technique. Time scale features have been extracted from the denoised TOFD signals and an artificial neural network for weld defect classification was developed. A multi layer feed forward network with BFGS quasi-Newton back propagation has been applied for classification of the signals. The effect of hidden layers on the network was analyzed. The optimum performance function for this network was also found.
Page(s) : 845-849
ISSN : 0976-5166
Source : Vol. 2, No.6