Abstract
Multi-objective optimization is becoming an essential stage in the choice of machining parameters. The objective of this paper was to optimize the choice of cutting parameters in terms of cutting speed, depth of cut, and feed rate during turning process of AISI 52100 hardened steel when multiple objectives were simultaneously taken into consideration like surface roughness, consumed power, cutting time or machining cost, productivity or metal removal rate and cutting forces. The turning process in this case was in dry conditions and the selected machining parameters have been investigated using full factorial design of experiments for three parameters (cutting speed, depth of cut, and feed rate). The relationship between parameters and performance responses were developed by using multiple linear regression analysis (MLR) and first-order empirical models were obtained. Analysis of variance (ANOVA) was employed to check the validity of the developed models within the limits of the factors that were being investigated and to test the significance of the above parameters. Thus, the obtained empirical models have been used to determine the optimal machining parameters with multi-objective optimization method based on weighting factors and genetic algorithm (GA) optimization method. Finally, an industrial example demonstrating the effectiveness of the proposed methodology was presented and confirmed the values when compared to the experimental results. This methodology should help the users to obtain the optimal process parameters for their application.
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References
Abouelatta OB, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118:269–277
Montgomery D C (1997) Design and analysis of experiments, 4th ed., John Wiley & Sons, NY
Davidson MJ, Balasubramanian K, Tagore GRN (2008) Surface roughness prediction of flow-formed AA6061 alloy by design of experiments. J Mater Process Technol 202(1–3):41–46
Paiva P, Ferreira JR, Balestrassi PP (2007) A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization. J Mater Process Technol 189:26–35
Horng J-T, Liu N-M, Chiang K-T (2008) Investigating the machinability evaluation of hadfield steel in the hard turning with Al2O3/TiC mixed ceramic tool based on the response surface methodology. J Mater Process Technol 208(1–3):532–541
Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design, 2nd edition, Cambridge University Press
Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18(5–6):343–354
Chibane H, Morandeau A, Serra R, Bouchou A, Leroy R (2013) Optimal milling conditions for carbon/epoxy composite material using damage and vibration analysis. Int J Adv Manuf Technol 68(5):1111–1121
Duchosal A, Serra R, Leroy R, Hamdi H (2015) Numerical optimization of the minimum quantity lubrication parameters by inner canalizations and cutting conditions for milling finishing process with Taguchi method. J Clean Prod 108:65–71
Corso LL, Zeilmann RP, Nicola GL, Missell FP, Gomes HM (2013) Using optimization procedures to minimize machining time while maintaining surface quality. Int J Adv Manuf Technol 65:1659–1667
Satishkumar S, Asokan P (2008) Selection of optimal conditions for CNC multitool drilling system using non-traditional techniques. Int J Machining Machinability Mater 31:190–207
Lippman Richard P (1987) An introduction to computing with neural nets, IEEE ASSP Magazine 4(2):4–22
Ghorbani H, Moetakef-Imani B (2016) Specific cutting force and cutting condition interaction modeling for round insert face milling operation. Int J Adv Manuf Technol 84(5–8):1705–1715
Marler RT, Aurora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim 26:369–395
Klingenberg W, and Singh, U P (2004). Principles for on-line monitoring of tool wear during sheet metal punching. In: Hinduja S. (eds) Proceedings of the 34th International MATADOR Conference. Springer. https://doi.org/10.1007/978-1-4471-0647-0_25
Sandvik Coromand (2007) Turning tools catalogue: practical handbook, English edition
Goupy J. and Creighton L (2007) Introduction to design of experiments with JMP examples, (3rd ed.). SAS Publishing
Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer-Verlag, New York
Sreejith PS, Ngoi BKA (2000) Dry machining: machining of the future. J Mater Process Technol 101(1–3):287–291
Krishnaiah PR (1982) Selection of variables under univariate regression models. Handbook Statistics 2:805–820
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester
Marler RT, Jasbir SA (2010) The weighted sum method for multi-objective optimization: some insights. Struct Multidiscip Optim 41(6):853–862
Zadeh LA (1963) Optimality and non-scalar-valued performance criteria. IEEE Trans Autom Control AC 8:59–60
Goldberg DE (1989) Genetic algorithms for search, optimization, and machine learning. Addison-Wesley, Reading
Dhabale R, Jatti VS, Singh TP (2014) Multi-objective optimization of turning process during machining of AlMg1SiCu using non-dominated sorted genetic algorithm. Procedia Mater Sci 6:961–966
Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput Integr Manuf 19(1–2):113–121
Saravanan R, Asokan P, Sachidanandam M (2002) A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. Int J Mach Tools Manuf 42:1327–1334
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Serra, R., Chibane, H. & Duchosal, A. Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel. Int J Adv Manuf Technol 99, 2025–2034 (2018). https://doi.org/10.1007/s00170-018-2373-3
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DOI: https://doi.org/10.1007/s00170-018-2373-3