Abstract
This study investigated the multi-response optimization of turning process for an optimal parametric combination to yield minimum cutting forces and surface roughness with maximum material removal rate (MRR) using the combination of Grey relational analysis (GRA) and Taguchi method. Nine experimental runs based on an orthogonal array of Taguchi method were performed to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of cutting process: cutting force, surface roughness and MRR. The Taguchi approach followed by Grey relational analysis to solve the multi-response optimization problem. The significance of factors on overall quality characteristics of the cutting process has also been evaluated quantitatively by the analysis of variance method (ANOVA). Optimal results have been verified through additional experiments. This shows proper selection of the cutting parameters produces, high material removal rate with better surface roughness and lower cutting force.
Kurzfassung
In der diesem Beitrag zugrunde liegenden Studie wurde eine Mehrfach-Antwort-Optimierung des Drehprozesses in Hinblick auf eine optimale Parameterkombination zur Erreichung minimaler Schnittkräfte und Oberflächenrauheiten bei maximaler Materialabtragsrate (MMR) durchgeführt, in dem die Grey Relational Analyse (GRA) mit der Taguchi Methode kombiniert wurde. Neun experimentelle Runs, die auf einem orthogonalen Feld der Taguchi-Methode basieren, wurden durchgeführt, um objektive Funktionen abzuleiten, die mittels der experimentellen Ergebnisse optimiert wurden. Die objektiven Funktionen wurden in Hinblick auf die Parameter des Schneidprozesses, nämlich der Schnittkraft, der Oberflächenrauheit und der MRR gewählt. Dem Taguchi-Ansatz folgte eine GRA um die Mehrfach-Antwort-Aufgabe zu lösen. Die Signifikanz der Faktoren auf die allgemeinen Qualitätsmerkmale des Schneidprozesses wurden ebenfalls quantitativ mittels der Varianzanalyse (ANOVA) ausgewertet. Die optimalen Ergebnisse wurden experimentell verifiziert. Eine richtige Auswahl der Schneidparameter zeigt eine hohe Materialabtragsrate mit besserer Oberflächenrauheit und niedrigerer Schnittgeschwindigkeit.
References
1 J. P.Fabricio, P. P.Anderson, P. B.Pedro, F. J.Roberto, B. S.Messias: Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi—s orthogonal arrays, Expert Systems with Applications39 (2012), pp. 7776–7787Search in Google Scholar
2 C.Basheer, U. A.Dabade, S. J.Suhas, V. V.Bhanuprasad: Modelling of surface roughness in precision machining of metal matrix composites using ANN, Journal of Materials Processing Technology197 (2008), pp. 439–444Search in Google Scholar
3 V. S.Sharma, S.Dhiman, R.Sehgal, S. K.Sharma: Estimation of cutting forces and surface roughness for hard turning using neural networks, Journal of Intelligent Manufacturing19 (2008), pp. 473–483Search in Google Scholar
4 Y.Karpat, T.Özel: Multi-objective optimization for turning processes using neural network modelling and dynamic neighborhood particle swarm optimization, International Journal of Advanced Manufacturing Technology, 35 (2008), pp. 234–247Search in Google Scholar
5 P. G.Benardos, G. C.Vosniakos: Prediction of surface roughness in CNC in machining: A review, International Journal of Machine Tools and Manufacture43 (2003), pp. 833–844Search in Google Scholar
6 T.Özel, Y.Karpat: Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools and Manufacture45 (2005), pp. 467–479Search in Google Scholar
7 Y.Kazancoglu, U.Esme, M.Bayramoglu, O.Guven, S.Ozgun, Multi-objective optimization of the cutting forces in turning operations using the Grey-based Taguchi method, Materiali in Tehnologije45 (2011), pp. 105–110Search in Google Scholar
8 W. H.Yang, Y. S.Tarng: Design Optimization of cutting parameters for turning operations based on the Taguchi method84 (1988), pp. 122–129Search in Google Scholar
9 D. C.Montgomery: Design and analysis of experiments, 7th Ed., Wiley, New York, USA (2009)Search in Google Scholar
10 P.Ross: Taguchi techniques for quality engineering, McGraw Hill, New York, USA (1991).Search in Google Scholar
11 P. L. B.Oxley: Modelling machining processes with a view to their optimization and the adaptive control of metal cutting machine tools, Robot. Comput.-Integrated Manuf.4 (1988), pp. 103–11910.1016/0736-5845(88)90065-8Search in Google Scholar
12 G.Chryssolouris, M.Guillot: A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining, ASME J. Eng. Ind.112 (1990), pp. 122–131Search in Google Scholar
13 Y.Yao, X. D.Fang: Modelling of multivariate time series for tool wear estimation in finish turning, Int. J. Mach. Tools Manuf.32 (1992), No. 4, pp. 495–508Search in Google Scholar
14 C.Zhou, R. A.Wysk: An integrated system for selecting optimum cutting speeds and tool replacement times, Int. J. Mach. Tools Manuf.32 (1992), No. 5, pp. 695–707Search in Google Scholar
15 M. S.Chua, M.Rahman, Y. S.Wong, H. T.Loh: Determination of optimal cutting conditions using design of experiments and optimization techniques, Int. J. Mach. Tools Manuf.33 (1993), No. 2, pp. 297–305Search in Google Scholar
16 A.Bendell, J.Disney, W. A.Pridmore: Taguchi Methods: Applications in World Industry, IFS Publications, UK (1989)Search in Google Scholar
17 D. C.Montgomery: Design and Analysis of Experiments, Wiley, Singapore (1991)Search in Google Scholar
18 S.Datta, A.Bandyopadhyay, P. K.Pal: Grey-based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding, Int. J. Adv. Manuf. Technol.39 (2008), No. 11, pp. 1136–1143Search in Google Scholar
19 U.Esme, M.Bayramoglu, Y.Kazancoglu, S.Özgun: Optimization of weld bead geometry in TIG welding process using Grey relation analysis and Taguchi method, Materiali in Tehnologije43 (2009), pp. 143–149Search in Google Scholar
20 D. S.Holmes, A. E.Mergen: Signal to Noise Ratio - What is the Right Size, www.qualitymag.com/.../Manuscript%20Holmes%20&%20Mergen.pdf, USA, (1996), pp. 1–6Search in Google Scholar
© 2012, Carl Hanser Verlag, München