On-line prediction of quality and shear strength of spacer pad welds of nuclear fuel pins by applying neural network analysis of acoustic emission signals

Abstract

In the present study, the development of an acoustic emission technique (AET) based methodology is reported for online prediction of quality and shear strength of spacer pad welds of nuclear fuel pins of pressurised heavy water reactors (PHWRs). The quality evaluation of spacer pad welds was made by classification of different weld categories using cluster analysis and artificial neural network (ANN) study of acoustic emission signals generated during welding. The ANN approach was also effective in arriving at the quantitative estimation of percentage correct classification between any two classes. For assessment of shear strength of individual coins of spacer pad welds by ANN, the properties of basic sigmoidal function were exploited and this could predict the strength of each coin with an accuracy of 97%. The results assume significance because instrumentation methodology is suitable for online application and complement the currently followed statistical quality control approaches for spacer pad weld assessment.