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Arabian Journal for Science and Engineering

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19.05.2021 | Research Article-Mechanical Engineering

A New Optimization Technique in Examining the Machinability of Sverker 21 Steel: Gray Relational Analysis-Based Genetic Algorithm

verfasst von: Cetin Ozay, Mehmet Altug, Hasan Ballikaya

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 12/2021

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Abstract

In this study, to determine the optimum parameters of the WEDM method, Taguchi experiment design, gray relational analysis (GRA) and gray relational analysis-based genetic algorithm (GRABGA) methods were used. In experimental studies, first, microstructure, microhardness and conductivity examinations of, both commercial and heat treated, Sverker 21 cold tool steel were performed. Then, the commercial and heat-treated Sverker 21 cold tool steel was processed with the WEDM method at the determined parameters. The effects of processing parameters on average surface roughness (Ra), kerf, and vibration were evaluated separately using Taguchi test design, GRA and GRABGA methods. It was determined that the homogeneous distribution of the carbides increased, the microhardness increased and the conductivity increased in the microstructure of the heat-treated sample compared to the commercial sample. In the processing of materials with WEDM, while the Taguchi test design method was used to evaluate the effects of processing parameters on the results were evaluated separately, GRA method was used to determine the grade of relationship between the results. In addition, the GRABGA method enabled the optimum values of the processing parameters to be determined not only on the basis of the level, but also on the intermediate values. It was found that the GRA relationship value was 0.8564 whereas the GRABGA relationship value was 0.8977. It was concluded that the GRABGA method provided better results than the GRA method in the correlation analysis of the results obtained in processing D2 cold tool steel with the WEMD method.

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Altuǧ, M.: Investigation of material removal rate (MRR) and wire wear ratio (WWR) for alloy Ti6Al4 V exposed to heat treatment processing in WEDM and optimization of parameters using grey relational analysis. Mater. Test. 58(9), 794–805 (2016)CrossRef

Altug, M.; Erdem, M.; Ozay, C.: Experimental investigation of kerf of Ti6Al4V exposed to different heat treatment processes in WEDM and optimization of parameters using genetic algorithm. Int. J. Adv. Manuf. Technol. 78(9–12), 1573–1583 (2015). https://doi.org/10.1007/s00170-014-6702-xCrossRef

Altuǧ, M.; Erdem, M.; Ozay, C.; Bozkir, O.: Surface roughness of Ti6AI4V after heat treatment evaluated by artificial neural networks. Mater. Test. 58(3), 189–199 (2016). https://doi.org/10.3139/120.110844CrossRef

Berns, H.: Comparison of wear resistant MMC and white cast iron. Wear 254(1–2), 47–54 (2003). https://doi.org/10.1016/S0043-1648(02)00300-9CrossRef

Bourithis, L.; Papadimitriou, G.D.; Sideris, J.: Comparison of wear properties of tool steels AISI D2 and O1 with the same hardness. Tribol. Int. 39(6), 479–489 (2006). https://doi.org/10.1016/j.triboint.2005.03.005CrossRef

Çiçek, A.; Ekici, E.; Uygur, İ; Akıncıoglu, S.; Kıvak, T.: Investigation of the effects of deep cryogenic treatment on tool life in drilling of AISI D2 cold work tool steel. SDU Int. J. Technol. Sci. 4(1), 1–9 (2012)

Das, D.; Dutta, A.K.; Ray, K.K.: Sub-zero treatments of AISI D2 steel: Part II. wear behavior. Mater. Sci. Eng., A 527(9), 2194–2206 (2010). https://doi.org/10.1016/j.msea.2009.10.071CrossRef

Goswami, A.; Kumar, J.: Investigation of surface integrity, material removal rate and wire wear ratio for WEDM of Nimonic 80A alloy using GRA and Taguchi method. Eng. Sci. Technol., Int. J. 17(4), 173–184 (2014). https://doi.org/10.1016/j.jestch.2014.05.002CrossRef

Gupta, P.; Gupta, R.D.; Khanna, R.; Sharma, N.: Effect of process parameters on kerf width in WEDM for HSLA using response surface methodology. J. Eng. Technol. 2(1), 1 (2012). https://doi.org/10.4103/0976-8580.94230CrossRef

10.

Gur, A.K.; Ozay, C.; Icen, B.: Evaluation of B4c/Ti coating layer, investigation of abrasive wear behaviors using Taguchi technique and response surface methodology. Surf. Rev. Lett. 1950225, 1–17 (2020). https://doi.org/10.1142/S0218625X19502251CrossRef

11.

Gür, A.K.; Kaya, S.: Abrasive wear resistance optimization of three different carbide coatings by the Taguchi method. Mater. Test. 59(5), 450–455 (2017). https://doi.org/10.3139/120.111020CrossRef

12.

Gür, A.K.; Yildiz, T.; Icen, B.: Theoretical evaluation of abrasive wear behavior of B 4 C/FeCrC coating layer evaluated by a Taguchi approach. Mater. Test. 62(7), 733–738 (2020). https://doi.org/10.3139/120.111540CrossRef

13.

Hamidzadeh, M.A.; Meratian, M.; Mohammadi Zahrani, M.: A study on the microstructure and mechanical properties of AISI D2 tool steel modified by niobium. Mater. Sci. Eng., A 556, 758–766 (2012). https://doi.org/10.1016/j.msea.2012.07.061CrossRef

14.

Han, F.; Jiang, J.; Dingwen, Yu.: Influence of machining parameters on surface roughness in finish cut of WEDM. Int. J. Adv. Manuf. Technol. 34(5–6), 538–546 (2007). https://doi.org/10.1007/s00170-006-0629-9CrossRef

15.

Hargrove, S.K.; Ding, D.: Determining cutting parameters in wire EDM based on workpiece surface temperature distribution. Int. J. Adv. Manuf. Technol. 34(3–4), 295–299 (2007). https://doi.org/10.1007/s00170-006-0609-0CrossRef

16.

Hasçalýk, A.; Çaydaş, U.: Experimental study of wire electrical discharge machining of AISI D5 tool steel. J. Mater. Process. Technol. 148(3), 362–367 (2004). https://doi.org/10.1016/j.jmatprotec.2004.02.048CrossRef

17.

Hewidy, M.S.; El-Taweel, T.A.; El-Safty, M.F.: Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM. J. Mater. Process. Technol. 169(2), 328–336 (2005). https://doi.org/10.1016/j.jmatprotec.2005.04.078CrossRef

18.

Ikram, A.; Mufti, N.A.; Saleem, M.Q.; Khan, A.R.: Parametric optimization for surface roughness, Kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment. J. Mech. Sci. Technol. 27(7), 2133–2141 (2013). https://doi.org/10.1007/s12206-013-0526-8CrossRef

19.

Jangra, K.; Jain, A.; Grover, S.: Optimization of multiple-machining characteristics in wire electrical discharge machining of punching die using grey relational analysis. J. Sci. Ind. Res. 69(8), 606–612 (2010)

20.

Kao, J.Y.; Tsao, C.C.; Wang, S.S.; Hsu, C.Y.: Optimization of the EDM parameters on machining Ti-6Al-4V with multiple quality characteristics. Int. J. Adv. Manuf. Technol. 47(1–4), 395–402 (2010). https://doi.org/10.1007/s00170-009-2208-3CrossRef

21.

Karabatak, M.; Kara, F.: Experimental optimization of surface roughness in hard turning of AISI D2 cold work tool steel. J. Polytech. 19(3), 349–355 (2016). https://doi.org/10.2339/2016.19.3CrossRef

22.

Chaubey, K.; Sujeet, S.S.; Singh, A.: Some investigations into machining of AISI D2 tool steel using wire electro discharge machining (WEDM) process. Mater. Today: Proc. 5(11), 24347–24357 (2018). https://doi.org/10.1016/j.matpr.2018.10.230CrossRef

23.

Kumar, S.; Grover, S.; Walia, R.S.: Effect of hybrid wire EDM conditions on generation of residual stresses in machining of HCHCr D2 tool steel under ultrasonic vibration. Int. J. Interact. Des. Manuf. 12(3), 1119–1137 (2018). https://doi.org/10.1007/s12008-018-0474-8CrossRef

24.

Kumar, S.; Nagraj, M.; Bongale, A., et al.: Deep cryogenic treatment of AISI M2 tool steel and optimisation of its wear characteristics using Taguchi‘s approach. Arab. J. Sci. Eng. 43, 4917–4929 (2018). https://doi.org/10.1007/s13369-018-3242-yCrossRef

25.

Kuriakose, S.; Shunmugam, M.S.: Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. J. Mater. Process. Technol. 170(1–2), 133–141 (2005). https://doi.org/10.1016/j.jmatprotec.2005.04.105CrossRef

26.

Niva, N.: FE simulation of ductile fracture in matrix type tool steel. Master’s Thesis, Lulea University of Technology, Lulea, Sweden (2006)

27.

Mahapatra, S.S.; Patnaik, A.: Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int. J. Adv. Manuf. Technol. 34(9–10), 911–925 (2007). https://doi.org/10.1007/s00170-006-0672-6CrossRef

28.

Manjaiah, M.; Laubscher, R.F.; Kumar, A.; Basavarajappa, S.: Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using taguchi-based utility approach. Int. J. Mech. Mater. Eng. 11(1), 1–9 (2016). https://doi.org/10.1186/s40712-016-0060-4CrossRef

29.

Okafor, U.C.: Mechanical Characterization of A2 and D2 Tool Steels by Nanoindentation. Dissertation, University of North Texas (2012)

30.

Özler, L.: Determination of optimum parameters in friction drilling of AISI 1010 steel by grey relational analysis. Fırat Univ. J. Eng. Sci. 31(2), 535–544 (2019)

31.

Pradhan, M.K.; Biswas, C.K.: Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISID2 Steel "NF and NN based prediction of responses in EDM of D2 steel. Int. J. Adv. Manuf. Technol. 50(5–8), 591–610 (2010). https://doi.org/10.1007/s00170-010-2531-8CrossRef

32.

Ramakrishnan, R.; Karunamoorthy, L.: Multi response optimization of wire EDM operations using robust design of experiments. Int. J. Adv. Manuf. Technol. 29(1–2), 105–112 (2006). https://doi.org/10.1007/s00170-004-2496-6CrossRef

33.

Shah, A.; Mufti, N.A.; Rakwal, D.; Bamberg, E.: Material removal rate, kerf, and surface roughness of tungsten carbide machined with wire electrical discharge machining. J. Mater. Eng. Perform. 20(1), 71–76 (2011). https://doi.org/10.1007/s11665-010-9644-yCrossRef

34.

Shandilya, P.; Jain, P.K.; Jain, N.K.: Parametric optimization during wire electrical discharge machining using response surface methodology. Procedia Eng. 38, 2371–2377 (2012). https://doi.org/10.1016/j.proeng.2012.06.283CrossRef

35.

Singh, H.; Khanna, R.: Parametric optimization of cryogenic-treated D-3 for cutting rate in wire electrical discharge machining. J. Eng. Technol. 1(2), 59 (2011). https://doi.org/10.4103/0976-8580.86633CrossRef

36.

Singh, V.; Bhandari, R.; Yadav, V.K.: An experimental investigation on machining parameters of AISI D2 steel using WEDM. Int. J. Adv. Manuf. Technol. 93(1–4), 203–214 (2017). https://doi.org/10.1007/s00170-016-8681-6CrossRef

37.

Şi̇ri̇n, E.; Turgut, Y.; Korkut, İ: The effect of cutting parameters on surface roughness and tool wear in the milling process of AISI D2 cold work tool steel with different hardness. Polytech. J. 15(1), 9–14 (2012)

38.

Toboła, D.; Brostow, W.; Czechowski, K.; Rusek, P.: Improvement of wear resistance of some cold working tool steels. Wear 382–383, 29–39 (2017). https://doi.org/10.1016/j.wear.2017.03.023CrossRef

39.

Toboła, D.; Kania, B.: Phase composition and stress state in the surface layers of burnished and gas nitrided Sverker 21 and Vanadis 6 tool steels. Surf. Coat. Technol. 353, 105–115 (2018). https://doi.org/10.1016/j.surfcoat.2018.08.055CrossRef

40.

Tosun, N.; Cogun, C.; Tosun, G.: A study on kerf and material removal rate in wire electrical discharge machining based on taguchi method. J. Mater. Process. Technol. 152(3), 316–322 (2004). https://doi.org/10.1016/j.jmatprotec.2004.04.373CrossRef

41.

Wang, J.; Guo, W.; Sun, H.; Li, H.; Gou, H.; Zhang, J.: Plastic deformation behaviors and hardening mechanism of M7C3 carbide. Mater. Sci. Eng., A 662, 88–94 (2016). https://doi.org/10.1016/j.msea.2016.03.062CrossRef

42.

Wilmes, S.; Kientopf, G.: Carbide dissolution rate and carbide contents in usual high alloyed tool steels at austenitizing temperatures between 900 °C and 1250 °C. In: 6th International Tooling Conference, pp. 533–541 (2002)

43.

Yuan, J.; Wang, K.; Tao, Yu.; Fang, M.: Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. Int. J. Mach. Tools Manuf. 48(1), 47–60 (2008). https://doi.org/10.1016/j.ijmachtools.2007.07.011CrossRef

Titel: A New Optimization Technique in Examining the Machinability of Sverker 21 Steel: Gray Relational Analysis-Based Genetic Algorithm
verfasst von: Cetin Ozay
Mehmet Altug
Hasan Ballikaya
Publikationsdatum: 19.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 12/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05721-5

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