Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process

Abstract

The monitoring of welding process is crucial for the development of a real time quality control system for the pulsed metal inert gas welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.