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Prediction of Best Weld Quality Using Artificial Neural Network

The aim of the proposed work is to use an artificial neural network(ANN) to predict the best weld made of two dissimilar metals i.e. MS (AISI 1040) and SS (AISI3014) fabricated by Gas tungsten arc welding (GTAW), also known as tungsten inert gas (TIG) welding. The process parameters used are weld travel speed, current, gas flow rate, and inclination. Experiments are designed and conducted as per the Taguchi method. Further MOORA method is used to find the best combination of input process parameters. The results of experiments are used to model the process using for artificial neural network. The performance ANN models is compared with experimental results and found the good correlation between the experimental results and the results provided by ANN.