alexa Estimation of Dependent Parameters of Saw Process
ISSN: 2168-9873

Journal of Applied Mechanical Engineering
Open Access

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Research Article

Estimation of Dependent Parameters of Saw Process

Aniruddha Ghosh*
Department of Mechanical Engineering, Government College of Engineering and Textile Technology, Berhampore, WB 742101, India
Corresponding Author : Aniruddha Ghosh, Assistant Professor
Department of Mechanical Engineering
Government College of Engineering and
Textile Technology Berhampore, WB 742101, India
E-mail: [email protected]
Received August 27, 2012; Accepted November 29, 2012; Published Deceber 03, 2012
Citation: Ghosh A (2012) Estimation of Dependent Parameters of Saw Process. J Appl Mech Eng 2:114. doi:10.4172/2168-9873.1000114
Copyright: © 2012 Ghosh A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

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.

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