Abstract
This study aims at finding a set of optimum solutions of cutting conditions for the machining responses of cutting temperature and surface roughness in hard turning of 42CrMo4 alloy steel at high-pressure coolant (HPC) condition. Comparative experimental investigations between dry and HPC cutting environments were performed to evaluate the stated responses concerning the factors of cutting speed, feed, and work-piece hardness. The full factorial method was employed for the experimental design. The measured value of cutting temperature and surface roughness was found in a reduced amount for HPC condition compared to dry cut for all of the machining runs. Empirical models were developed by response surface methodology for the responses of HPC-assisted machining. The ANOVA result indicated that cutting speed and hardness has the greatest effect on cutting temperature and surface roughness, respectively. Design of experiment (DoE) based optimization was carried out that results in the best optimum settings of 147 m/min cutting speed, 0.12 mm/rev feed rate and 42HRC work-piece hardness. Genetic algorithm based multi-objective optimization was then performed that simultaneously minimizes both of the response models. Within the constraints of experimental design, the optimal set resulted at the range of 86–165 m/min cutting speed, 0.12–0.13 mm/rev feed rate and HRC 42–44 work-piece hardness.
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References
Senthil Kumar, A., Raja Durai, A., Sornakumar, T.: Machinability of hardened steel using alumina based ceramic cutting tools. Int. J. Refract. Metals Hard Mater. 21(3–4), 109–117 (2003). https://doi.org/10.1016/S0263-4368(03)00004-0
Umbrello, D., Rizzuti, S., Outeiro, J.C., Shivpuri, R., M’Saoubi, R.: Hardness-based flow stress for numerical simulation of hard machining AISI H13 tool steel. J. Mater. Process. Technol. 199(1–3), 64–73 (2008). https://doi.org/10.1016/j.jmatprotec.2007.08.018
Bogdan, A., Gavrilă, C.: The economic efficiency of replacing grinding with hard turning. Recent 18(2), 71–76 (2017)
Thamizhmanii, S., Hasan, S.: Measurement of surface roughness and flank wear on hard martensitic stainless steel by CBN and PCBN cutting tools. J. Achiev. Mater. Manuf. Eng. 31(2), 415–421 (2008)
List, G., Nouari, M., Géhin, D., Gomez, S., Manaud, J.P., Le Petitcorps, Y., Girot, F.: Wear behaviour of cemented carbide tools in dry machining of aluminium alloy. Wear 259(7–12), 1177–1189 (2005). https://doi.org/10.1016/j.wear.2005.02.056
Totten, G.: Handbook of Residual Stress and Deformation of Steel. ASM International, Materials Park, Ohio (2002)
Matsumoto, Y., Barash, M.M., Liu, C.R.: Residual stress in the machined surface of hardened steel. In: High Speed Machining Conference, ASME WAM. pp.193–204. (1984)
Outeiro, J.C., Umbrello, D., M’Saoubi, R.: Experimental and numerical modelling of the residual stresses induced in orthogonal cutting of AISI 316L steel. Int. J. Mach. Tools Manuf. 46(14), 1786–1794 (2006). https://doi.org/10.1016/j.ijmachtools.2005.11.013
Ulutan, D., Erdem Alaca, B., Lazoglu, I.: Analytical modelling of residual stresses in machining. J. Mater. Process. Technol. 183(1), 77–87 (2007). https://doi.org/10.1016/j.jmatprotec.2006.09.032
Nasr, M.N.A., Ng, E.-G., Elbestawi, M.A.: A modified time-efficient FE approach for predicting machining-induced residual stresses. Finite Elem. Anal. Des. 44(4), 149–161 (2008). https://doi.org/10.1016/j.finel.2007.11.005
Kramar, D., Kopač, J.: High pressure cooling in the machining of hard-to-machine materials. J. Mech. Eng. 55(11), 685–694 (2009)
Kamruzzaman, M., Rahman, S.S., Ashraf, Md.Z.I., Dhar, N.R.: Modeling of chip–tool interface temperature using response surface methodology and artificial neural network in HPC-assisted turning and tool life investigation. Int. J. Adv. Manuf. Technol. 90, 1547–1568 (2017). https://doi.org/10.1007/s00170-016-9467-6
Machado, A.R., Wallbank, J., Pashby, I.R., Ezugwu, E.O.: Tool performance and chip control when machining tI6Al4v and inconel 901 using high pressure coolant supply. Mach. Sci. Technol. 2(1), 1–12 (1998). https://doi.org/10.1080/10940349808945655
Kaminski, J., Alvelid, B.: Temperature reduction in the cutting zone in water-jet assisted turning. J. Mater. Process. Technol. 106(1–3), 68–73 (2000). https://doi.org/10.1016/S0924-0136(00)00640-3
Senthil Kumar, A., Rahman, M., Ng, S.L.: Effect of high-pressure coolant on machining performance. Int. J. Adv. Manuf. Technol. 20(2), 83–91 (2002). https://doi.org/10.1007/s001700200128
Ezugwu, E.O.: Key improvements in the machining of difficult-to-cut aerospace superalloys. Int. J. Mach. Tools Manuf. 45(12–13), 1353–1367 (2005). https://doi.org/10.1016/j.ijmachtools.2005.02.003
Globočki-Lakić, G., Sredanović, B., Kramar, D., Kopač, J.: Machinability of C45e steel - application of minimum quantity lubrication and high pressure jet assisted machining techniques. T FAMENA 40(2), 45–58 (2016). https://doi.org/10.21278/TOF.40204
Mia, M., Khan, M.A., Dhar, N.R.: High-pressure coolant on flank and rake surfaces of tool in turning of Ti-6Al-4V: investigations on surface roughness and tool wear. Int. J. Adv. Manuf. Technol. 90(5–8), 1825–1834 (2017). https://doi.org/10.1007/s00170-016-9512-5
Umbrello, D., Filice, L.: Improving surface integrity in orthogonal machining of hardened AISI 52100 steel by modeling white and dark layers formation. CIRP Ann. 58(1), 73–76 (2009). https://doi.org/10.1016/j.cirp.2009.03.106
Rao, C.J., Rao, D.N., Srihari, P.: Influence of cutting parameters on cutting force and surface finish in turning operation. Procedia Eng. 64, 1405–1415 (2013). https://doi.org/10.1016/j.proeng.2013.09.222
Singla, A., Singh, T.: Optimization of process parameters on CNC turning. Int. J. Curr. Eng. Technol. 6(2), 657–661 (2016)
Özel, T., Karpat, Y., Figueira, L., Davim, J.P.: Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J. Mater. Process. Technol. 189(1–3), 192–198 (2007). https://doi.org/10.1016/j.jmatprotec.2007.01.021
Suresh, R., Basavarajappa, S.: Effect of process parameters on tool wear and surface roughness during turning of hardened steel with coated ceramic tool. Procedia Mater. Sci. 5, 1450–1459 (2014). https://doi.org/10.1016/j.mspro.2014.07.464
Bartarya, G., Choudhury, S.K.: Effect of cutting parameters on cutting force and surface roughness during finish hard turning AISI52100 grade steel. Procedia CIRP 1, 651–656 (2012). https://doi.org/10.1016/j.procir.2012.05.016
Kant, G., Rao, V.V., Sangwan, K.S.: Predictive modeling of turning operations using response surface methodology. AMM 307, 170–173 (2013). https://doi.org/10.4028/www.scientific.net/AMM.307.170
Mia, M., Dhar, N.R.: Effectof high pressure coolant jet on cutting temperature, tool wear and surface finish in turning hardened (HRC 48) steel. J. Mech. Eng. 45(1), 1–6 (2015). https://doi.org/10.3329/jme.v45i1.24376
Zahia, H., Athmane, Y.M., Lakhdar, B., Tarek, M.: On the application of response surface methodology for predicting and optimizing surface roughness and cutting forces in hard turning by PVD coated insert. Int. J. Ind. Eng. Comput. 6(2), 267–284 (2015). https://doi.org/10.5267/j.ijiec.2014.10.003
Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000). https://doi.org/10.1162/106365600568158
Amouzgar, K., Bandaru, S., Andersson, T., Ng, A.H.C.: Metamodel-based multi-objective optimization of a turning process by using finite element simulation. Eng. Optim. (2019). https://doi.org/10.1080/0305215X.2019.1639050
Mishra, A., Gangele, D.A.: Multi-objective optimization in turning of cylindrical bars of AISI 1045 steel through Taguchi’s method and utility concept. Int. J. Sci. 12(1), 28–36 (2013)
Zerti, A., Yallese, M.A., Meddour, I., Belhadi, S., Haddad, A., Mabrouki, T.: Modeling and multi-objective optimization for minimizing surface roughness, cutting force, and power, and maximizing productivity for tempered stainless steel AISI 420 in turning operations. Int. J. Adv. Manuf. Technol. 102(1–4), 135–157 (2019). https://doi.org/10.1007/s00170-018-2984-8
Janahiraman, T.V., Ahmad, N.: Multi objective optimization for turning operation using hybrid extreme learning machine and multi objective genetic algorithm. IJET 7(4.35), 876–879 (2018). https://doi.org/10.14419/ijet.v7i4.35.26273
Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006). https://doi.org/10.1016/j.ress.2005.11.018
Srinath, R.N., Tirumalaa, D., Gajjelaa, R., Das, R.: ANN and RSM approach for modelling and multi objective optimization of abrasive water jet machining process. Decis. Sci. Lett. 7, 535–548 (2018)
Ameur, T.: Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning. J King Saud Univ Eng Sci. 33, 259–265 (2021)
Öztürk, O., Kalyoncu, M., Ünüvar, A.: Multi objective optimization of cutting parameters in a single pass turning operation using the bees algorithm. In: 1st International Conference on Advances in Mechanical and Mechatronics Engineering, (2018)
Pawar, P.J., Khalkar, M.Y.: Multi-objective optimization of wire-electric discharge machining process using multi-objective artificial bee colony algorithm. Adv. Eng. Optim. Intell. Tech. 949, 39–46 (2020)
Yang, S.H., Natarajan, U.: Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches. Int. J. Adv. Manuf. Technol. 49, 773–784 (2010)
Deb, K., Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds.) Evolutionary Multi-Criterion Optimization, pp. 67–81. Springer, Berlin, Heidelberg (2001)
Onwubolu, G.C., Kumalo, T.: Optimization of multipass turning operations with genetic algorithms. Int. J. Prod. Res. 39(16), 3727–3745 (2001). https://doi.org/10.1080/00207540110056153
Quiza Sardiñas, R., Rivas Santana, M., Alfonso Brindis, E.: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Eng. Appl. Artif. Intell. 19(2), 127–133 (2006). https://doi.org/10.1016/j.engappai.2005.06.007
Petkovic, D., Radovanovic, M.: Using genetic algorithms for optimization of turning machining process. JESR. 19(1), 47–55 (2016). https://doi.org/10.29081/jesr.v19i1.139
Gjelaj, A., Berisha, B., Smaili, F.: Optimization of turning process and cutting force using multiobjective genetic algorithm. Univ. J. Mech. Eng. 7(2), 64–70 (2019)
Khan, A.: Effect of high pressure coolant jets in turning TI-6AL-4V alloy with specialized designed nozzle. http://lib.buet.ac.bd:8080/xmlui/handle/123456789/3763 (2015)
Pencheva, T., Atanassov, S.A.: Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. Bioautomation 13(4), 257–264 (2009)
Wahde, M.: Biologically Inspired Optimization Methods: An Introduction. WIT Press, Southampton, UK, Boston, MA (2008)
Cao, Y.J., Wu, Q.H.: Teaching genetic algorithm using MATLAB. Int. J. Elect. Enging. Educ. 36(2), 139–153 (1999)
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The authors would like to acknowledge the support of the Directorate of Advisory Extension and Research Services (DAERS), BUET, Bangladesh for allowing laboratory facilities in the central Machine Shop, BUET to perform the research work.
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Saha, S., Zaman, P.B., Tusar, M.I.H. et al. Multi-objective genetic algorithm (MOGA) based optimization of high-pressure coolant assisted hard turning of 42CrMo4 steel. Int J Interact Des Manuf 16, 1253–1272 (2022). https://doi.org/10.1007/s12008-022-00848-7
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DOI: https://doi.org/10.1007/s12008-022-00848-7