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ANN Based Model Development for Material Removal Rate in Dry Turning in Indian Context

Authors: Mangesh R. Phate, V. H. Tatwawadi


This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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[1] Iwona Piotrowska • Christina Brandt • Hamid Reza Karimi • Peter Mass” Mathematical model of micro turning process” International Journal of Advanced Manufacturing Technology, 2009, PP.33–40.
[2] Funda Kahraman "The use of response surface methodology for the prediction and analysis of surface roughness of AISI 4140 steel.” Materials and technology, 2009, PP. 267–270.
[3] Atul Kumar, Dr. Sudhir Kumar and Dr. Rohit Garg, "Statistical Modeling of surface roughness in turning process” International Journal of Engineering Science and Technology (IJEST) Vol. 3 No. 5 May 2011,PP 4246-4252.
[4] Jeffrey B. Dahmus and Timothy G. Gutowski, "An environmental analysis of machining” 2004 ASME International Mechanical Engineering Congress and RD&D Expo November 13-19, 2004, Anaheim, California USA.
[5] H. Soleimanimehr, M. J. Nategh , S. Amini, "Modeling of Surface Roughness in Vibration Cutting by Artificial Neural Network” World Academy of Science, Engineering and Technology 52 2009,PP 386-391.
[6] Vinayak Neelakanth Gaitonde & S. R. Karnik & Luis Figueira & J. Paulo Davim,” Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling” Int J Adv Manuf Technol (2011) 52:101–114 DOI 10.1007/s00170-010-2714-3.
[7] Petropoulos G., Ntziantzias I. and Anghel C., in: International Conference on Experiments/ Process/ System Modelling/ Simulation/Optimization, Athens, 2005, PP.25-34.
[8] H. Schenck Jr., Theories of Engineering a experimentation, McGraw Hill Book Co ,New York,1954,PP.40-50.
[9] Sundaram R.M., An application of goal programming technique in metal cutting, Int. J. Prod. Res., 1978, PP. 375-382.
[10] Agapiou J.S., The optimization of machining operations based on a combined criterion, Part 1 The use of combined objectives in single-pass operations, Part 2: Multi-pass operations. J. Eng Ind., Trans. ASME, 1(14), 500–513 (1992).
[11] Brewer R.C. and Rueda R., A simplified approach to the optimum selection of machining parameters, Eng Dig., 1963, PP.133–150.
[12] Klir G.J and, Yuan B., Fuzzy system and fuzzy logic – theory and practice (Englewood Cliffs, NJ: Prentice Hall), (1998).
[13] Petropoulos P.G., Optimal selection of machining rate Variable by geometric programming. J Prod. Res., 1973, PP. 305–314.
[14] Phate M.R., Tatwawadi V.H., Modak J.P., Formulation of A Generalized Field Data Based Model For The Surface Roughness of Aluminum 6063 In Dry Turning Operation, New York Science Journal, 2012,PP. 38-46.
[15] Tatwawadi V.H., Modak J.P. and Chibule S.G., Mathematical Modeling and simulation of working of enterprise manufacturing electric motor, International Journal of Industrial Engineering,2010, PP.341-351.
[16] Walvekar A.G. and Lambert B.K., An application of geometric programming to machining variable selection. Int. J. Prod. Res., 1970, PP.38-45.
[17] Gilbert W.W., Economics of machining. In Machinin – Theory and practice. Am. Soc. M1950, PP. 476–480.
[18] Muwell K.F.H., Nature of Ergonomics, Ergonomics (Man In His Working Environment), Chapman and Hall, London, New York, 1956, PP.69-85.