Abstract
Optimization of machining parameters considering multiple responses flank wear, surface roughness, and material removal rate (MRR) simultaneously are performed using response surface methodology (RSM). The workpiece material chosen for turning is AISI 1045, medium carbon steel, and uncoated carbide tool inserts. Twenty experiments are designed based on face-centered center composite design for three numerical parameters such as cutting speed, feed rate, and depth of cut. In this work, wear at the flank face of the cutting tool insert and surface roughness at the machined surface are to be minimized, whereas the MRR has to be maximized. With the obtained optimum condition, a confirmation experiment is performed and the experimental results obtained are flank wear of 0.118 mm, surface roughness of 3.27 μm, and MRR of 187.35 gm/min, which shows that prediction using RSM is within the acceptable range. Along with the combined optimization of these responses, a quadratic empirical model is generated for each response. An evolutionary optimization algorithm, firefly algorithm, is applied to determine the optimum machining parameters for the chosen objective of lowering flank wear and increasing MRR within a specific surface roughness value.
Similar content being viewed by others
References
Rao S.S.: Engineering Optimization Theory and Practice, 4th edn. Wiley, New Jersey (2009)
Rao R.V.: Advanced Modeling and Optimization of Manufacturing Processes. Springer, London (2011)
Groover M.P.: Fundamentals of modern manufacturing: materials, processes and systems, 4th edn. Wiley, USA (2010)
Neseli S., Yaldiz S., Turke E.: Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44, 580–587 (2011)
Asilturk I., Neseli S.: Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis. Measurement 45, 785–794 (2012)
Makadia A.J., Nanavati J.I.: Optimisation of machining parameters for turning operations based on response surface methodology. Measurement 46, 1521–1529 (2013)
Noordin M.Y., Venkatesh V.C., Sharif S., Elting S., Abdullah A.: Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. J. Mater. Process. Technol. 145, 46–58 (2004)
Horng J.-T., Liu N.-M., Chiang K.-T.: Investigating the machinability evaluation of Hadfield steel in the hard turning with Al 2 O 3 /TiC mixed ceramic tool based on the response surface methodology. J. Mater. Process. Technol. 208, 532–541 (2008)
Fnides B., Yallese M.A., Mabrouki T., Rigal J.F.: Application of response surface methodology for determining cutting force model in turning hardened AISI H11 hot work tool steel. Sadhana 36(1), 109–123 (2011)
Aouici H., Yallese M.A., Fnides B., Chaoui K., Mabrouki T.: Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness. J. Mech. Sci. Technol. 25(11), 2843–2851 (2011)
Saini S., Ahuja I.S., Sharma V.S.: Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools. Int. J. Precis. Eng. Manuf. 13(8), 1295–1302 (2012)
Mandal N., Doloi B., Mondal B.: Force Prediction Model of Zirconia Toughened Alumina (ZTA) Inserts in Hard Turning of AISI 4340 Steel Using Response Surface Methodology. Int. J. Precis. Eng. Manuf. 13(9), 1589–1599 (2010)
Senthilkumar, N.; Tamizharasan, T.: Effect of tool geometry in Turning AISI 1045 steel: experimental investigation and FEM analysis. Arab. J. Sci. Eng. (2014). doi:10.1007/s13369-014-1054-2
Yildiz Ali R.: Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf. Sci. 220, 399–407 (2013)
Yildiz A.R.: Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int. J. Adv. Manuf. Technol. 64(1–4), 55–61 (2013)
Yildiz A.R.: Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl. Soft Comput. 13(3), 1433–1439 (2013)
Suhail Adeel H., Ismail N., Wong S.V., Abdul Jalil N.A.: Surface roughness identification using the grey relational analysis with multiple performance characteristics in turning operations. Arab. J. Sci. Eng. 37(4), 1111–1117 (2012)
Ganesan H., Mohankumar G.: Optimization of machining techniques in CNC turning centre using genetic algorithm. Arab. J. Sci. Eng. 38(6), 1529–1538 (2013)
Tharik B.M.: A comparative study of firefly algorithm and cuckoo search algorithm in optimizing turning operation with constrained parameters. Int. J. Eng. Res. Technol. 2(4), 1701–1706 (2013)
Raja, S.B.; Pramod, C.V.S.; Krishna, K.V.; Ragunathan, A.; Vinesh, S.: Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm. Eng. Comput. (2013). doi:10.1007/s00366-013-0320.3
Raja S.B., Narayanan N.S., Pramod C.V.S., Ragunathan A., Vinesh S.R., Krishna K.V.: Optimization of constrained machining parameters in turning operation using firefly algorithm. J. Appl. Sci. 12(10), 1038–1042 (2012)
Senthilkumar N., Tamizharasan T.: Impact of interface temperature over flank wear in hard turning using carbide inserts. Proced. Eng. 38, 613–621 (2012)
Yusup N., Zain A.M., Hashim S.Z.M.: Evolutionary techniques in optimizing machining parameters: review and recent applications (2007–2011). Expert Syst. Appl. 39(10), 9909–9927 (2012)
Yusup, N.; Sarkheyli, A.; Zain, A.M.; Hashim, S.Z.M.; Ithnin, N.: Estimation of optimal machining control parameters using artificial bee colony. J. Intell. Manuf. (2013). doi:10.1007/s10845-013-0753-y
Zain A.M., Haron H., Sharif S.: Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst. Appl. 37, 4650–4659 (2010)
Yang X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Lunver Press, UK (2010)
Montgomery D.C.: Design and Analysis of Experiments, 8th edn. Wiley, USA (2013)
Lin Y.C., Tsao C.C., Hsu C.Y., Hung S.K., Wen D.C.: Evaluation of the characteristics of the microelectrical discharge machining process using response surface methodology based on the central composite design. Int. J. Adv. Manuf. Technol. 62, 1013–1023 (2012)
Sivasakthivel P.S., Velmurugan V., Sudhakaran R.: Prediction of vibration amplitude from machining parameters by response surface methodology in end milling. Int. J. Adv. Manuf. Technol. 53, 453–461 (2011)
Suresh Kumar B., Baskar N.: Integration of fuzzy logic with response surface methodology for thrust force and surface roughness modeling of drilling on titanium alloy. Int. J. Adv. Manuf. Technol. 65, 1501–1514 (2013)
El-Taweel T.A., Gouda S.A.: Performance analysis of wire electrochemical turning process—RSM approach. Int. J. Adv. Manuf. Technol. 53, 181–190 (2011)
John M.R.S., Vinayagam B.K.: Optimization of ball burnishing process on tool steel (T215Cr12) in CNC machining centre using response surface methodology. Arab. J. Sci. Eng. 36(7), 1407–1422 (2011)
Oberg E., Jones F.D., Horton H.L., Ryffel H.H.: Machinery’s Handbook, 28th edn. Industrial Press, New York (2008)
Kumar, A.; Kumar, V.; Kumar, J.: Multi-response optimization of process parameters based on response surface methodology for pure titanium using WEDM process. Int. J. Adv. Manuf. Technol. (2013). doi:10.1007/s00170-013-4861-9
Yang, X.S.: Firefly algorithms for multimodal optimization. In: 5th Symposium on Stochastic Algorithms, Foundation and Applications (SAGA 2009), LNCS, vol. 5792, pp. 169–178 (2009)
Puri A.B., Banerjee Simul: Multiple-response optimisation of electrochemical grinding characteristics through response surface methodology. Int. J. Adv. Manuf. Technol. 64, 715–725 (2013)
Natarajan U., Periyanan P.R., Yang S.H.: Multiple-response optimization for micro-end milling process using response surface methodology. Int. J. Adv. Manuf. Technol. 56, 177–185 (2011)
Gopalakannan S., Senthilvelan T.: EDM of cast Al/SiC metal matrix nanocomposites by applying response surface method. Int. J. Adv. Manuf. Technol. 67(1–4), 485–493 (2013)
Sivasakthivel, P.S.; Sudhakaran, R.: Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. (2012). doi:10.1007/s00170-012-4652-8
Elangovan S., Anand K., Prakasan K.: Parametric optimization of ultrasonic metal welding using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. 63, 561–572 (2012)
Kilickap E., Huseyinoglu M., Yardimeden A.: 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, 79–88 (2011)
Pradhan, M.K.: Estimating the effect of process parameters on surface integrity of EDMed AISI D2 tool steel by response surface methodology coupled with grey relational analysis. Int. J. Adv. Manuf. Technol. (2012). doi:10.1007/s00170-012-4630-1
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Senthilkumar, N., Tamizharasan, T. & Gobikannan, S. Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel. Arab J Sci Eng 39, 8015–8030 (2014). https://doi.org/10.1007/s13369-014-1320-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-014-1320-3