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Statistics report
Apr. 2021
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 94
Paper Published : 2094
No. of Authors : 5972
  Journal Paper


Paper Title :
Application Of Back Propagation Neural Network For Prediction Of Some Shell Moulding Parameters

Author :Megha Saxena

Article Citation :Megha Saxena , (2015 ) " Application Of Back Propagation Neural Network For Prediction Of Some Shell Moulding Parameters " , International Journal of Mechanical and Production Engineering (IJMPE) , pp. 1-5, Volume-3,Issue-12

Abstract : The present study has used an ANN to predict shell molding performances – permeability number and transverse strength- of sand moulds employed in hot metal casting. A back propagation ANN is used. This study utilized actual experimental data in which input conditions – CaCo3% .MnO3%, Dwell time and temperature – had been varied and the corresponding resulting performances were recorded. This data was used to both train and validate the ANN with the eventual objective of identifying optimum input conditions that would deliver desirable moulding performance output – the two targeted shell molding properties. The investigation used MATLAB 7.0 while the ANN’s training and validation steps empirically varied learning rate, momentum rate, the number of hidden layers and the number of hidden neurons in each layer. The trained neural network was observed to be capable of predicting the output within 10% error range of their corresponding observed values. Keywords- Shell Moulding, Back Propagation Neural Network.

Type : Research paper

Published : Volume-3,Issue-12


DOIONLINE NO - IJMPE-IRAJ-DOIONLNE-3457   View Here

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