Weldbead Parametric Estimation of SAW Process Through Neural Network
Authors
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Dept., Kalyani Govt Engg. College, Kaiyani ,IN
A. Ghosh -
Mecon Limited, Ranchi ,INS. Mukherjee
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Department of Mechanical Engg. & Mining Machinery Engg. Indian School of Mines University, Dhanbad ,INS. Chattopadhyaya
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Department of Mechanical Engg. & Mining Machinery Engg. Indian School of Mines University, Dhanbad ,INP. K. Sarkar
Abstract
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. 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.
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.
