Abstract
This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.
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References
Dewes R, Aspinwall D (1997) A review of ultra high speed milling of hardened steels. J Mater Process Technol 69:1–17
Baskar N, Asokan P, Saravanan R, Prabhaharan G (2006) Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm. J Mater Process Technol 174:239–249
Cakir M, Ensarioglu C, Demirayak I (2009) Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. J Mater Process Technol 209:102–109
Yusup N, Zain A, Hashim S (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications. Expert Syst Appl 39:9909–9927
Vivancos J, Luis C, Costa L, Ortiz J (2004) Optimal machining parameters selection in high speed milling of hardened steels for injection moulds. J Mater Process Technol 155–156:1505–1512
Ӧktem H, Erzurumlu T, Kurtaran H (2005) Application of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170:11–16
Yildiz A (2009) A novel hybrid immune algorithm for optimizing of machining parameters in milling operations. Robot Comput Integr Manuf 25:261–270
Zain A, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machine using Artificial Neural Network. Expert Syst Appl 37:1755–1768
Huang B, Chen J (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21:339–347
Benardos P, Vosnaikos G (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844
Rashid A, Gan S, Muhammad N (2009) Mathematical modeling to predict surface roughness in CNC milling. World Acad Sci Eng Technol 53:393–396
Ozcelik B, Bayramoglu M (2006) The statistical modeling of surface roughness in high-speed flat end milling. Int J Mach Tools Manuf 46:1395–1402
Ghani J, Choudhury I, Hassan H (2004) Application of Taguchi method in the optimization of end milling parameters. J Mater Process Technol 145:84–92
Vivancos J, Luis C, Ortiz H (2005) Analysis of factors affecting the high-speed side milling of hardened die steels. J Mater Process Technol 162–163:696–701
Park C, Kown K, Kim W, Min B, Park S, Sung I, Yoon Y, Lee K, Lee J, Seok J (2009) Energy consumption reduction technology in manufacturing—a selective review of policies standards and research. Int J Precis Eng Manuf 10(5):151–173
Pineda-Henson R, Culaba A (2004) A diagnostic model for green productivity assessment of manufacturing processes. Int J LCA 9(6):379–386
He LB, Zhang X, Gao H, Liu X (2012) A modeling method of task-oriented energy consumption for machining manufacturing system. J Clean Prod 23(1):167–174
Rahimifard S, Seow Y, Childs T (2010) Minimising embodied product energy to support energy efficient manufacturing. CIRP Ann 59(1):25–28
Fang K, Uhan N, Zaho F, Sutherland J (2011) A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30(4):234–240
Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Annals-Manuf Technol 60:37–40
Newman S, Nassehi A, Imani-Asrai R, Dhokia V (2012) Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol 5:127–136
Bhushan R (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254
Zain A, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37:4650–4659
Saffar R, Razfar R (2010) Simulation of end milling operation for predicting cutting forces to minimize tool deflection by genetic algorithm. Mach Sci Technol 14(1):81–101
Parent L, Songmene V, Kenné J (2007) A generalised model for optimising an end milling operation. Prod Plan Control 18(4):319–337
Sultana I, Dhar N (2010) GA based multi objective optimization of the predicted models of cutting temperature, chip reduction co-efficient and surface roughness in turning AISI 4320 steel by uncoated carbide insert under HPC condition. Proceedings of the 2010 International Conference on Mechanical. Ind Manuf Technol MIMT 2010:161–167
Duran O, Barrientos R, Consalter L (2007) Multi objective optimization in machining operations. Analysis and Design of Intelligent Systems using Soft Computing Techniques 455–462
Xie S, Pan L (2010) Selection of machining parameters using genetic algorithms. ICCSE 2010—5th International Conference on Computer Science and Education, Final Program and Book of Abstracts 1147–1150
Kilickap E, Huseyinoglu M, Yardimeden A (2011) Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 52(1–4):79–88
Jain N, Jain V (2007) Optimization of electro-chemical machining process parameters using genetic algorithms. Mach Sci Technol 11(2):235–258
Mohanasundararaju N, Sivasubramanian R, Alagumurthi N (2008) Optimisation of work roll grinding using response surface methodology and evolutionary algorithm. Int J Manuf Res 3(2):236–251
Gao Q, Zhang Q, Su S, Zhang J (2008) Parameter optimization model in electrical discharge machining process. J Zhejiang Univ Sci A 9(1):104–108
Kumar A, Khan M, Thiraviam R, Sornakumar T (2006) Machining parameters optimization for Alumina based ceramic cutting tools using genetic algorithm. Mach Sci Technol 10:471–489
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Alrashdan, A., Bataineh, O. & Shbool, M. Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm. Int J Adv Manuf Technol 73, 1201–1212 (2014). https://doi.org/10.1007/s00170-014-5921-5
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DOI: https://doi.org/10.1007/s00170-014-5921-5