EFFECTS OF INPUT PARAMETERS ON WELD BEAD GEOMETRY OF SAW PROCESS

Because of its high quality and reliability, Submerged Arc Welding (SAW) is one of the chief metals joining process employed in industry. In this paper, an attempt has been taken to develop a model to predict the yield characteristics (weld bead parameters) of Submerged Arc Welding (SAW) process with the help of neural network technique and analysis of various process control variables and important weld bead parameters in SAW. The SAW process has been chosen for this application because of the complex set of variables and high set up cost involved in the process as well as its significant application in the manufacturing of critical equipments which have a lot of economic and social implications. Also an attempt has been taken for prediction of out put variable of SAW process in this paper. For this purpose Neural Network model has been applied. Under this study the neural network model has been trained according to the actual inputs and outputs. After completing training, the desired inputs have been given to the model and it gives the estimated output value. And according to this we can also estimate the error between the actual and predicted results. Neural network is implemented here because of having remarkable ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Hence a trained neural network can be thought of as an "expert" in the category of information it has been given to analyses.