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Modeling and Simulation of Productivity in the Turning of Ferrous and Nonferrous Material using Artificial Neural Network and Response Surface Methodology

Author Affiliations

  • 1Dept. Of Mechanical Engg, TSSM’s, PVPIT, Bavdhan, Pune, Maharashtra, INDIA
  • 2 Dr.Babasaheb Ambedkar College of Engg and Research, Nagpur, Maharashtra, INDIA
  • 3 Mechanical Engg, Dept, Priyadarshni College of Engineerig, Nagpur, INDIA

Res. J. Engineering Sci., Volume 2, Issue (3), Pages 37-44, March,26 (2013)

Abstract

Traditional machining is a complex phenomenon which includes the workers who operates the machines and his working environment such as atmospheric parameters, work piece parameters, cutting process parameters, tool parameters and etc In the India and other country the majority of total machining operation are still executed manually which needs to be focused and develop a mathematical model referred as Field data based Model) to identify the strengths and weaknesses of the present method. The formulated field data based Model (FDBM ) correlates the various input parameters with the output parameters . The present paper aimed to propose improvement in methods of performing these activities by developing mathematical simulation from data collected while the work was actually being executed in the field. Once the generalized model using all possible parameters developed, the weaknesses of the present method identified and improvement is possible. The main contribution of this paper is to develop the mathematical model for the turning of ferrous and nonferrous material. The validation of the formulated Field Data Based Mathematical model (FDBM) is achieved by comparing with the Artificial Neural Network and response surface model. The aim of the paper is to find out the mathematical model for the productivity i.e. machining time and the machining cost required for turning the ferrous and nonferrous work piece. Out of so many parameters mentioned above we would like to find out which of these are most important for increasing the productivity. Simultaneously it would be interesting to know influence of one parameter over the other.

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