OPTIMIZATION OF PROCESS PARAMETERS FOR GAS METAL ARC WELDING OF DISSIMILAR AA7075 AND AA6063 ALUMINIUM ALLOYS USING ARTIFICIAL NEURAL NETWORKS (ANN)

Abstract

The present investigations deal with the effect and optimization of gas metal arc welding parameters on the mechanical properties in welding of dissimilar AA7075 and AA6063 Aluminum alloys. The process parameters used are current, voltage and gas flow rate and Taguchi experimental design method were followed. Tensile strength and Impact strength have been found for the optimum welding parameters. Further an Artificial Neural Network model was developed for the analysis and simulation of the correlation between process parameters and mechanical properties. The input for the model is current, voltage and gas flow rate and the output for the model is Tensile and Impact strength. The combined influence of current, voltage and gas flow rate on the mechanical properties of the joint was simulated. The model can calculate tensile strength and impact strength as functions of process parameters. Lastly a comparison was made between the measured and calculated value and it was found that the calculated results were in agreement with the measured data.