International Journal of Mechanical and Production Engineering (IJMPE)
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Statistics report
Feb. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 128
Paper Published : 2371
No. of Authors : 6757
  Journal Paper

Paper Title :
Application of Artificial Neural Network for Prediction of Process Parameters of the Machining Processes

Author :D. B. Gohil

Article Citation :D. B. Gohil , (2022 ) " Application of Artificial Neural Network for Prediction of Process Parameters of the Machining Processes " , International Journal of Mechanical and Production Engineering (IJMPE) , pp. 12-18, Volume-10,Issue-11

Abstract : Abstract – The objective of this research work is to apply the Artificial Neural Network (ANN) technique to the prediction of process parameters effectively to take the advantage in today's highly competitive world where manufacturing costs are sharply increasing due to increased cost of the raw materials, manpower and energy; short duration between demand and supply, challenge of reducing global warming, carbon footprint, environmental impact due to manufacturing activities. The surface finish plays a very vital role in the functioning of any component in the system. Therefore, in this study, the output parameter is surface roughness while three main process parameters speed, feed and depth of cut of machining process are input parameters which are responsible for obtaining desired surface finish on the work surface. The cutting tool parameters could be assumed to be constant because of a very high hardness ratio of tool to work material. The surface roughness of the machined work pieces was measured using a surface roughness tester. For the ANN software, the input parameters were speed, feed and depth of cut while the output parameter was surface roughness. To predict the surface roughness, the ANN model has been configured through feed forward back-propagation network and also a neural fitting tool has been used for the experimental data for the prediction of the resultant surface finish. Keywords - Artificial Neural Network, Surface roughness, Prediction, Machining, Modeling, Optimization, Profitability.

Type : Research paper

Published : Volume-10,Issue-11


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