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Tytuł artykułu

Experimental Investigation of Manufacturing Variables Effect on Electrical Discharge Machining of Titanium Alloy

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Titanium alloys are considered one of the materials required in industries. They can be used in various fields due to their high strength and corrosion resistance, but titanium alloys are considered difficult to machine using traditional methods. EDM is used to machine a workpiece using electrical discharges to cut hard materials that are challenging to cut with traditional methods. Therefore, this paper focuses on the machine of a high-strength material-titanium alloy Ti-6Al-4V and studies the influence of cutting process variables on the metal removal rate, tool wear rate, and surface roughness of the samples. The samples matrix form is created depending on the design of experiments method. Pulse-on time, discharge current, and gap process variables with three levels create mathematical models to predict the responses without conducting practical experiments. The results proved that machining variables impacted the responses, which were proven through results data analysis and the interaction plots. Also, the maximum error between experimental and predicted values using the mathematical model was 0.022 (mg/min) for the MRR, 1.719 (mg/min) for the TWR, and 0.334 (μm) for the Ra.
Wydawca
Rocznik
Tom
Strony
202--211
Opis fizyczny
Bibliogr. 44 poz., rys., tab.
Twórcy
autor
  • Department of Production Engineering and Metallurgy University of Technology-Iraq
  • Department of Production Engineering and Metallurgy University of Technology-Iraq
  • Department of Production Engineering and Metallurgy University of Technology-Iraq
  • Ford Motor Company Michigan, USA
Bibliografia
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  • [6] Pramanik, A., Basak, A.K., Littlefair, G., Debnath, S., Prakash, C., Singh, M.A., Marla, D. Singh, R.K. 2020. Methods and variables in Electrical discharge machining of titanium alloy – A review. Heliyon, 6(12), pp. 1-15.
  • [7] Qudeiri, J.E.A., Zaiout, A., Mourad, A.H.I., Abidi, M.H. Elka-seer, A. 2020. Principles and characteristics of different EDM processes in machining tool and die steels. Applied sciences, 10(6), p.2082.
  • [8] Asif, N., Saleem, M.Q. Farooq, M.U. 2023. Performance evaluation of surfactant mixed dielectric and process optimization for electrical discharge machining of titanium alloy Ti6Al4V. CIRP Journal of Manufacturing Science and Technology, 43, pp.42-56.
  • [9] Mahapatra, K.K., Biswal, S., Tripathy, S., Thatoi, D.N. Satapathy, P. 2022, August. Optimization of EDM Process Parameters in Machining of Titanium Alloy Used for Aerospace Applications. In International Conference on Recent Advances in Mechanical Engineering Research and Development. Singapore: Springer Nature Singapore, pp. 163-172.
  • [10] Thakur, A., Rao, P.S. Khan, M.Y. 2021. Study and optimization of surface roughness parameter during electrical discharge machining of titanium alloy (Ti-6246). Materials Today: Proceedings, 44, pp. 838-847.
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  • [13] Kalaiarasi, K., Senthilkumar, C., Balamurugan, M. Arokia-Dass, R. 2022. Micro-electrical discharge machining of Titanium alloy (Ti-6Al-4V) by Sawtooth pulse current. International Journal of Electrochemical Science, 17(4), p. 220442.
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  • [19] Singh, N.K., Singh, Y. Sharma, A. 2021. Experimental investigation of flushing approaches on EDM machinability during machining of titanium alloy. Materials Today: Proceedings, 38, pp. 139-145.
  • [20] Gautam, N., Goyal, A. Sharma, S.S., Oza, A.D. Kumar, R. 2022. Study of various optimization techniques for electric discharge machining and electrochemical machining processes. Materials Today: Proceedings, 57, pp.615-621.
  • [21] Kumar, S. Gupta, T. 2023. A review of electrical discharge machining (EDM) and its optimization techniques. Materials Today: Proceedings.
  • [22] Kumar, R., Roy, S., Gunjan, P., Sahoo, A., Sarkar, D.D. Das, R.K. 2018. Analysis of MRR and surface roughness in machining Ti-6Al-4V ELI titanium alloy using EDM process. Procedia Manufacturing, 20, pp. 358-364.
  • [23] Rouniyar, A.K. Shandilya, P. 2018. Multi-Objective optimization using Taguchi and grey relational analysis on machining of Ti-6Al-4V alloy by powder mixed EDM process. Materials Today: Proceedings, 5(11), pp. 23779-23788.
  • [24] Panda, S.N., Pattanaik, A.K., Patel, A.K., Nayak, S., Patra, P. Bagal, D.K. 2023. Process parameter optimization for machining of Ti-6Al-4V using WASPAS and multi-objective genetic algorithm along with exponential trend line analysis. Materials Today: Proceedings.
  • [25] Gugulothu, B., Rao, G.K.M., Rao, D.H., Kumsa, D.K. Kassa, M.B. 2021. Experimental results on EDM of Ti-6Al-4V in drinking water with Graphite powder concentration. Materials Today: Proceedings, 46, pp. 234-242.
  • [26] Gugulothu, B., Rao, G.K.M. Bezabih, M. 2021. Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy. Materials Today: Proceedings, 46, pp. 89-98.
  • [27] Agarwal, N., Irshad, M., Singh, M.R. Singh, G. 2022. Optimization of material removal rate of Ti-6Al-4V using Rao-1 algorithm. Materials Today: Proceedings, 62, pp. 6722-6726.
  • [28] Phani, K.V.S., Mishra, S.B., Nayak, S.K., Satpathy, M.P., Das, D.K. Nanda, B.K. 2023. Multi objective optimization of EDM process parameters for HCHCr steel. Materials Today: Proceedings.
  • [29] Verma, V. Sahu, R. 2017. Process parameter optimization of die-sinking EDM on Titanium grade – V alloy (Ti-6Al-4V) using full factorial design approach. Materials today: proceedings, 4(2), pp. 1893-1899.
  • [30] Verma, V. Sajeevan, R. 2015. Multi process parameter optimization of diesinking EDM on titanium alloy (Ti6-Al4-V) using Taguchi approach. Materials Today: Proceedings, 2(4-5), pp. 2581-2587.
  • [31] Agarwal, N., Singh, M.R., Singh, G. Yadav, R. 2023. Optimization of EDM parameters using Ananya algorithm: A new advanced optimization technique. Materials Today: Proceedings.
  • [32] Bhowmick, S., Mondal, R., Sarkar, S., Biswas, N., De, J. Majumdar, G. 2023. Parametric optimization and prediction of MRR and surface roughness of titanium mixed EDM for Inconel 718 using RSM and fuzzy logic. CIRP Journal of Manufacturing Science and Technology, 40, pp. 10-28.
  • [33] Kumar, A., Vivekananda, K. Prasad, A.R. 2022. Experimental investigation and parametric optimization during EDM of Titanium grade 9 using MOORA-fuzzy integrated multi-objective Genetic algorithm. Materials Today: Proceedings, 62, pp. 4473-4479.
  • [34] Singh, S.K., Prabhakar, S., Rao, D.K. Khare, S.K. 2022. Optimization of Machining parameters in EDM of Ti6-Al-4V alloy using Taguchi Grey relational analysis Methodology. Materials Today: Proceedings, 59, pp. 1231-1235.
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  • [36] Dikshit, M.K., Singh, S., Pathak, V.K., Saxena, K.K., Agrawal, M.K., Malik, V., Hazim Salem, K. Khan, M.I. 2023. Surface characteristics optimization of biocompatible Ti6Al4V with RCCD and NSGA II using die sinking EDM. Journal of Materials Research and Technology, 24, pp. 223-235.
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  • [38] Hasda, R.K. 2013. Optimization of EDM Process Parameters through Teaching Learning Based Optimization Algorithm (Master thesis).
  • [39] Singh, N.K., Singh, Y. Sharma, A. 2021. Experimental investigation of flushing approaches on EDM machinability during machining of titanium alloy. Materials Today: Proceedings, 38, pp. 139-145.
  • [40] Jayaraman, P., Mahesh, L. Senthil, V. 2015. Optimization of cutting parameters in turning of AA6351 using response surface methodology and genetic algorithm. IJAER, 10(23), pp. 43905-43911.
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  • [42] Enzi, A. Mynderse, J.A. 2017, November. Optimization of process parameters applied to a prototype selective laser sintering system. In ASME International Mechanical Engineering Congress and Exposition, (58356), p. V002T02A022.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bb174001-0977-4c91-8e8e-950017c18c54
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