Investigations on the effects of nitrogen gas in CNC machining of SS304 using Taguchi and Firefly Algorithm

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences






No. 1


Prasanth, P. : Department of Mechanical Engineering, Tagore Institute of Engineering and Technology, Deviyakurichi, Salem – 636112, Tamilnadu, India ; Sekar, T. : Department of Mechanical Engineering, Government College of Technology, Coimbatore – 641013, Tamilnadu, India ; Sivapragash, M. : Department of Mechanical Engineering, Universal College of Engineering and Technology, Vallioor, Tirunelveli – 627117, Tamilnadu, India



SS304 alloy ; turning ; Taguchi method ; optimization ; Firefly Algorithm

Divisions of PAS

Nauki Techniczne




  1.  Ch.Y. Nee, M.S. Saad, A.M. Nor, M.Z. Zakaria, and M.E. Baharudin, “Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution”, Int. J. Adv. Manuf. Technol. 97(1‒4), 1541‒1555 (2018).
  2.  B. Naveena, S.S. MariyamThaslima, V. Savitha, B. Naveen Krishna, D. Samuel Raj, and L. Karunamoorthy, “Simplified MQL System for Drilling AISI 304 SS using Cryogenically Treated Drills”, Mater. Manuf. Process. 32 (15), 1679‒1684 (2017).
  3.  D. Murat, C. Ensarioglu, N. Gursakal, A. Oral, and M.C. Cakir, “Surface roughness analysis of greater cutting depths during hard turning”, Mater. Test. 59 (9), 795‒802 (2017).
  4.  D. Tanikić, V. Marinković, M. Manić, G. Devedžić, and S. Ranđelović, “Application of response surface methodology and fuzzy logic basedsystem for determining metal cutting temperature”, Bull. Pol Ac.: Tech. 64(2),435‒445 (2016).
  5.  M. Dhananchezian, M. Rishabapriyan, G. Rajashekar, and S. Sathya Narayanan, “Study the Effect of Cryogenic Cooling on Machinability Characteristics During Turning Duplex Stainless Steel 2205”, Mater. Today: Proc. 5, 12062–12070 (2018).
  6.  C.A. Bolu, O.S. Ohunakin, E.T. Akinlabi, and D.S. Adelekan, “A Review of Recent Application of Machining Techniques, based on the Phenomena of CNC Machining Operations”, Elsevier Procedia Manuf. 35, 1054‒1060 (2019).
  7.  D. Kondayyaand and A. Gopala Krishna, “An integrated evolutionary approach for modelling and optimisation of CNC end milling process”, Int. J. Comput. Integr. Manuf. 25(11), 1069‒1084 (2012).
  8.  W.A. Jensen, “Confirmation Runs in Design of Experiments”, J. Qual. Technol. 48(2), 162‒177 (2016).
  9.  S. Amini, H. Khakbaz, and A. Barani, “Improvement of Near-Dry Machining and Its Effect on Tool Wear in Turning of AISI 4142”, Mater. Manuf. Process. 30, 241‒247 (2015).
  10.  E. Natarajan, V. Kaviarasan, W.H. Lim, S.S. Tiang, S. Parasuraman, and S. Elango, “Non-dominated sorting modified teaching– learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)”, J. Intell. Manuf. 31, 911–935 (2020), doi: 10.1007/s10845-019-01486-9.
  11.  V. Kaviarasan, R. Venkatesan, and E. Natarajan, “Prediction of surface quality and optimization of process parameters in drilling of Delrin using neural network”, Prog. Rubber Plast. Recycl. Technol. 35(3), 149–169 (2019).
  12.  N Senthilkumar, T. Ganapathy, and T. Tamizharasan, “Optimisation of machining and geometrical parameters in turning process using Taguchi method”, Aust. J. Mech. Eng.12 (2), 233‒246 (2016).
  13.  F. Kahraman, “Optimization of cutting parameters for surface roughness in turning of studs manufactured from AISI 5140 steel using the Taguchi method”, Mater. Test. 59 (1), 77‒80 (2017).
  14.  J. Rajaparthiban and A.N. Sait, “Application of the grey-based Taguchi method and Deform-3D for optimizing multiple responses in turning of Inconel 718”, Mater. Test. 60(9), 907‒912 (2018).
  15.  T. Kıvak and Ş. Mert, “Application of the Taguchi technique for the optimization of surface roughness and tool life during the milling of Hastelloy C22”, Mater. Test. 59(1), 69‒76 (2017).
  16.  R.N. Yadav, “A Hybrid Approach of Taguchi-Response Surface Methodology for Modeling and Optimization of Duplex Turning Process”, Measurement 100, 131‒138 (2016).
  17.  D. Brahmeswararao, K. Venkatarao, and A.G. Krishna, “A hybrid approach to multi response optimization of micro milling process parameters using Taguchi method-based graph theory and matrix approach (GTMA) and utility concept”, Measurement 114, 332‒339 (2018).
  18.  P. Raja, R. Malayalamurthi, and M. Sakthivel, “Experimental investigation of cryogenically treated HSS tool in turning on AISI1045 using fuzzy logic – Taguchi approach”, Bull. Pol Ac.: Tech. 67(4),687‒696 (2019).
  19.  G.V. Chakaravarthy, S. Marimuthu, and A. Naveen Sait, “Comparison of Firefly algorithm and Artificial Immune System algorithm for lot streaming in m-machine flow shop scheduling”, Int. J. Comput. Intell. Syst. 5(6), 1184‒1199 (2012).
  20.  X.S. Yang, Firefly algorithm in Engineering Optimization, John Wiley & Sons, New York, USA (2010).
  21.  X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimization”, Int. J. Bio-Inspired Comput. 2(2), 78‒84 (2010).
  22.  S. Kamarian, M. Shakeriand, and M.H. Yas, “Thermal buckling optimization of composite plates using firefly algorithm”, J. Exp. Theor. Artif. Intell. 29(4) 878‒794 (2016).
  23.  N.A. Al-Thanoon, O.S. Qasim, and Z.Y. Algamal, “A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics”, Chemometrics Intell. Lab. Syst. 184, 142‒152 (2019).
  24.  A.F. Zubair, M. Salman, and A. Mansor, “Embedding firefly algorithm in optimization of CAPP turning machining parameters for cutting tool selections”, Comput. Ind. Eng. 135, 317‒325 (2019).
  25.  T. Sekar, M. Arularasu, and V. Sathiyamoorthy, “Investigations on the effects of Nano-fluid in ECM of die steel”, Measurement 83, 38‒43 (2016).
  26.  E. Nas and B. Öztürk, “Optimization of surface roughness via the Taguchi method and investigation of energy consumption when milling spheroidal graphite cast iron materials”, Mater. Test. 60(5), 519‒525 (2018).
  27.  G. Samtaşand and S. Korucu, “Optimization of Cutting Parameters in Pocket Milling of Tempered and Cryogenically Treated 5754 Aluminum Alloy”, Bull. Pol Ac.: Tech. 67(4), 697‒707 (2019).
  28.  E. Hüner, “Optimization of axial flux permanent magnet generator by Taguchi experimental method”, Bull. Pol Ac.: Tech. 68(3), 409‒419 (2020).
  29.  Ş. Ertürk and G. Samtaş, “Design of grippers for laparoscopic surgery and optimization ofexperimental parameters for maximum tissue weight holding capacity”, Bull. Pol Ac.: Tech. 67(6), 1125‒1132 (2019).
  30.  J.A. Shukor, S. Said, R. Harun, S. Husinand, and Ab. Kadir, “Optimising of machining parameters of plastic material using Taguchi method”, Adv. Mater. Process. Technol. 2(1), 50‒56 (2016).
  31.  S. Shankar, T. Mohanraj, and S.K. Thangarasu, “Multi-response milling process optimization using the Taguchi method coupled to grey relational analysis”, Mater. Test. 58(5), 462‒470 (2016).
  32.  S. Jannet, P.K. Mathews, and R. Raja, “Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology”, Bull. Pol Ac.: Tech. 63(4), 851‒855 (2015).
  33.  J. Kwiecień and B. Filipowicz, “Firefly algorithm in optimization of queueing systems”, Bull. Pol Ac.: Tech. 60(2), 363‒368 (2012).
  34.  Z. Liu, X. Li, D. Wu, Z. Qian, P. Feng, and Y. Rong, “The development of a hybrid firefly algorithm for multi-pass grinding process optimization”, J. Intell. Manuf. 30(6), 2457‒2472 (2019).
  35.  J. Kwiecień and B. Filipowicz, “Comparison of firefly and cockroach algorithms in selected discreteand combinatorial problems”, Bull. Pol Ac.: Tech. 62(4), 797‒804 (2014).
  36.  M.C. Shaw, Metal Cutting Principles, Second Edition, Oxford University Press, New York (2004).
  37.  A. Elddein, I. Elshwain, M. Handawi, N. Redzuan, M.Y. Noordin, and D. Kurniawan, “Performance Comparison between Dry and Nitrogen Gas Cooling when Turning Hardened Tool Steel with Coated Carbide”, Appl. Mech. Mater. 735, 65‒69 (2015).
  38.  D. Lazarevic, M. Madića, P. Jankovića, and A. Lazarević, “Cutting Parameters Optimization for Surface Roughness in Turning Operation of Polyethylene (PE) Using Taguchi Method”, Tribol. Ind. 34(2), 68‒73, 2012.
  39.  N. Senthilkumar, T. Tamizharasan, and S. Gobikannan, “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).
  40.  A.H. Tazehkandi, M. Shabgard, and F. Pilehvarian, “Application of liquid nitrogen and spray mode of biodegradable vegetable cutting fluid with compressed air in order to reduce cutting fluid consumption in turning Inconel 740”, J. Clean Prod. 108 (part A), 90‒103 (2015).






DOI: 10.24425/bpasts.2020.136211


Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; No. 1; e136211