Journal of Applied Mechanical Engineering

Abstract

Estimation of Dependent Parameters of Saw Process

Aniruddha Ghosh

This paper is an attempt to develop a model to predict the output responses of Submerged Arc Welding (SAW) process with the help of neural network technique. Also a mathematical model has been developed to study the effects of input variable (i.e. current, voltage, travel speed) on output responses (i.e. reinforcement height, weld bead width, metal deposition rate). SAW process has been chosen for this application because of the complex set of variables 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. Under this study the neural network model is trained accordingto the actual inputs and outputs. When the training is completed then the desired inputs are 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.