Abstract
This paper focuses on two different models, namely regression mathematical and artificial neural network (ANN) models for predicting surface roughness. In the present work, surface roughness is taken as the response (output) variable measured during milling, while helix angle, spindle speed, feed and depth of cut are taken as input parameters. The design of experiments (DOE) technique is developed for four factors at five levels to conduct experiments. Experiments have been conducted for measuring surface roughness based on the DOE technique in a vertical machining centre on AISI 304 steel using an uncoated solid carbide end mill cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed-forward back-propagation ANN for the prediction of surface roughness. Predicted values of response by both models, i.e. regression and ANN, are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of surface roughness within the trained range.
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References
Bagci E., Aykut S.: A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6). Int. J. Adv. Manuf. Technol. 29, 940–947 (2006)
Yang J.L., Chen J.C.: A systematic approach for identifying optimum surface roughness performance in end-milling operations. J. Ind. Technol. 17(2), 1–8 (2001)
Shather, S.K.; Ibrheem, A.F.: Prediction of surface roughness in end-milling with multiple regression model. Eng. Tech. 26(3), 326–337 (2008)
Kurt M., Bagci E., Kaynak Y.: Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes. Int. J. Adv. Manuf. Technol. 40, 458–469 (2009)
Thangavel P., Selladurai V.: An experimental investigation on the effect of turning parameters on surface roughness. Int. J. Manuf. Res. 3(3), 285–300 (2008)
Hayajneh M.T., Tahat M.S., Bluhm J.: A study of the effects of machining parameters on the surface roughness in the end-milling process. Jordan J. Mech. Ind. Eng. 1(1), 1–5 (2007)
Routara B.C., Bandyopadhyay A., Sahoo P.: Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation. Int. J. Adv. Manuf. Technol. 40, 1166–1180 (2009)
Ginta T.L., Nurul Amin A.K.M., Mohd Radzi H.C.D., Mohd Amri Lajis.: Development of surface roughness models in end milling titanium alloy Ti-6Al-4V using uncoated tungsten carbide inserts. Eur. J. Sci. Res. 28(4), 542–551 (2009)
Alauddin M.E.I., Baradie M.A., Hashmi M.S.J.: Computer-aided analysis of a Surface roughness model for end milling. J. Mater. Process. Technol. 55, 123–127 (1995)
Ertekin Y.M., Kwon Y., Tseng T.L.: Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. Int. J. Mach. Tools Manuf. 43, 897–904 (2003)
Bhattacharya A., Das S., Majumder P., Batish A.: Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Prod. Eng. Res. Dev. 3, 31–40 (2009)
Suresh Kumar Reddy N., Venkateswara Rao P.: Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int. J. Adv. Manuf. Technol. 26, 1202–1210 (2005)
Ansalam Raj, T.G.; Narayanan Namboothiri, V.N.: An improved genetic algorithm for the prediction of surface finish in dry turning of SS 420 materials. Int. J. Adv. Manuf.Technol. doi:10.1007/s00170-009-2197-2 (2009)
Yang L.D., Chen J.C., Chow H.M., Lin C.T.: Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations. Int. J. Adv. Manuf. Technol. 28, 236–248 (2006)
Tsao C.C.: Grey–Taguchi method to optimize the milling parameters of aluminum alloy. Int. J. Adv. Manuf. Technol. 40, 41–48 (2009)
Ozcelik B., Oktem H., Kurtaran H.: Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int. J. Adv. Manuf. Technol. 27, 234–241 (2005)
Brezocnik M., Kovacic M., Ficko M.: Prediction of surface roughness with genetic programming. J.Mater. Process. Technol. 157(−158), 28–36 (2004)
Chang C.K., Lu H.S.: Study on the prediction model of surface roughness for side milling operations. J. Mater. Process. Technol. 29, 867–878 (2006)
Lo S.P.: An adaptive-network based fuzzy inference system for prediction of work piece surface roughness in end milling. J. Mater. Process Technol. 142, 665–675 (2003)
Pal S.K., Chakraborty D.: Surface roughness prediction in turning using artificial neural network. Neural Comput. Appl. 14, 319–324 (2005)
Sivasakthivel P.S., Vel Murugan V., Sudhakaran R.: Experimental evaluation of surface roughness for end milling of Al 6063: response surface and neutral network model. Int. J. Manuf. Res. 7(1), 9–25 (2012)
Kadirgama K., Noor M.M., Rahman M.M.: Optimization of surface roughness in end milling using potential support vector machine. Arab. J. Sci. Eng. 37(8), 2269–2275 (2012)
Yang J.L., Chen J.C.: A systematic approach for identifying optimum surface roughness performance in end-milling operations. J. Ind. Technol. 17(2), 1–8 (2001)
Milton Shaw M.C.: Metal Cutting Principles. Oxford University Press, New York (2005)
Montgomery D.C.: Design and Analysis of Experiments. Wiley, New York (2005)
Yang I.J., Bibby M.J., Chandel R.S.: Linear regression equations for modeling the submerged arc welding process. J. Mater. Process Technol. 39, 33–42 (1993)
Vishal, S.S.; Suresh D.; Rakesh S.; Sharma, S.K.: Estimation of cutting forces and surface roughness for hard turning using neural networks. J. Intell. Manuf. 19(4), 473–483 (2008)
Hagan M.T., Demuth H.B., Beale M.: Neural Network Design. Thomson Learning. Vikas Publishing House, India (1996)
Hammerstrom D.: Working with neural networks. IEEE Spectr. 30, 46–53 (1993)
Kannan T., Murugan N.: Artificial neural network modelling of weld bead geometry and dilution in flux cored arc welding. Int. J. Join Mater. 18(2), 46–51 (2006)
MATLAB 7.6.: The Math Work Inc. (2008)
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Kalidass, S., Palanisamy, P. Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models. Arab J Sci Eng 39, 8065–8075 (2014). https://doi.org/10.1007/s13369-014-1346-6
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DOI: https://doi.org/10.1007/s13369-014-1346-6