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Optimizing the Surface Parameters of Titanium (Ti-6Al-4V) Alloy Specimens Using WEDM Process Based on Taguchi-DEAR Algorithm

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Języki publikacji
EN
Abstrakty
EN
Because of its excellent mechanical qualities and weldability, titanium alloy is used in many different biomedical applications. Wire electrical discharge machining may be used to machine materials with such greater strengths and intricate forms. Using Taguchi-Data Envelopment Analysis-based Ranking (DEAR) approach and zinc-diffused coated brass wire electrode to improve Titanium alloy machining was the goal of this research project. The quality metrics that were taken into consideration were sur-face roughness, kerf width, and material removal rate. Among the selected factors, with an error of 2.7%, the optimal configuration of input factors was determined to be 130 µs (Ton), 40 µs (Toff), 50 V (SV), 6 A (IP), and 8 Kg (WT). Due to its relevance in the process of deionization, the Ton is the high-est influential parameter for creating quality measurements.
Twórcy
  • Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India
  • Advanced Manufacturing Institute, King Saud University, P.O. Box 800, Riyadh,11421, Saudi Arabia
  • Department of Mechanical Engineering, Bharath Institute of Higher Education and Research, Tambaram, Chennai, 600073, India
  • Advanced Manufacturing Institute, King Saud University, P.O. Box 800, Riyadh,11421, Saudi Arabia
  • Advanced Manufacturing Institute, King Saud University, P.O. Box 800, Riyadh,11421, Saudi Arabia
  • Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
  • Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland
Bibliografia
  • 1. Karmiris-Obratański P., Papazoglou E.L., LeszczyńskaMadej B., Zagórski K., Markopoulos A.P. Surface and subsurface quality of titanium grade 23 machined by electro discharge machining. Materials. 2022; 15: 164. https://doi.org/10.3390/ma15010164.
  • 2. Arrazola P.J., Garay A., Iriarte L.M., Armendia M., Marya S., Maitre F.L. Machinability of titanium alloys (Ti6Al4V and Ti555.3). J. Mater. Process. Technol. 2009; 209: 2223–2230. https://doi.org/10.1016/j.jmatprotec.2008.06.020.
  • 3. Dandekar C.R., Shin Y.C., Barnes J. Machinability improvement of titanium alloy (Ti–6Al–4V) via LAM and hybrid machining. Int. J. Mach. Tool. Manuf. 2010; 50: 174–182. https://doi.org/10.1016/j.ijmachtools.2009.10.013.
  • 4. Rashid R.A.R., Sun S., Wang G., Dargusch M.S. The effect of laser power on the machinability of the Ti-6Cr-5Mo-5V-4Al beta titanium alloy during laser assisted machining. Int. J. Mach. Tool. Manuf. 2012; 63: 41–43. https://doi.org/10.1016/j.ijmachtools.2012.07.006.
  • 5. Karkalos N.E., Karmiris-Obratański P, Kudelski R, Markopoulos A.P. Experimental study on the sustainability assessment of AWJ machining of Ti-6Al-4V using glass beads abrasive particles. Sustainability. 2021; 13: 8917. https://doi.org/10.3390/su13168917.
  • 6. Geethapriyan T., Kalaichelvan K., Muthuramalingam T., Rajadurai A. Performance analysis of process parameters on machining α-β titanium alloy in electrochemical micromachining process. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 2018; 232: 1577–1589. https://doi.org/10.1177/0954405416673103.
  • 7. Ohkubo C., Watanabe I., Ford J.P., Nakajima H., Hosoi T., Okabe T. The machinability of cast titanium and Ti–6Al–4V. Biomaterials. 2000; 21: 421–428. https://doi.org/10.1016/S0142-9612(99)00206-9.
  • 8. Papazoglou E.L., Markopoulos A.P., Papaefthymiou S., Manolakos D.E. Electrical discharge machining modeling by coupling thermal analysis with deformed geometry feature. Int. J. Adv. Manuf. Technol. 2019; 103: 4481–4493. https://doi.org/10.1007/s00170-019-03850-8.
  • 9. Papazoglou E.L., Karmiris-Obratański P., Karkalos N.E., Thangaraj M., Markopoulos A.P. Theoretical and experimental analysis of plasma radius expansion model in EDM: a comprehensive study. Int. J. Adv. Manuf. Technol. 2023; 126: 2429–2444. https://doi.org/10.1007/s00170-023-11292-6.
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  • 12. Thangaraj M., Ramamurthy A., Sridharan, K., Ashwin S. Analysis of surface performance measures on WEDM processed titanium alloy with coated electrodes. Mater. Res. Express. 2018; 5: 126503. https://doi.org/10.1088/2053-1591/aade70.
  • 13. Ramamurthy A., Sivaramakrishnan R., Thangaraj M., Venugopal S. Performance analysis of wire electrodes on machining Ti-6Al-4V alloy using wire electrical discharge machining process. Mach. Sci. Technol. 2015; 19: 577–593. https://doi.org/10.1080/10910344.2015.1085314.
  • 14. Lin Y.C., Cheng C.H., Su B.L., Hwang L.R. Machining characteristics and optimization of machining parameters of SKH 57 high-speed steel using electrical-discharge machining based on taguchi method. Mater. Manuf. Process. 2006; 21: 922–929. https://doi.org/10.1080/03602550600728133.
  • 15. Lin Y.C., Wang A.C., Wang D.A., Chen C.C. Machining performance and optimizing machining parameters of Al2O3–TiC ceramics using EDM based on the taguchi method. Mater. Manuf. Process. 2006; 24: 667–674. https://doi.org/10.1080/10426910902769285.
  • 16. Nanthakumar P., Rajadurai A., Muthuramalingam T. Multi Response optimization on mechanical properties of silica fly ash filled polyester composites using Taguchi-Grey relational analysis. Silicon. 2018; 10: 1723–1729. https://doi.org/10.1007/s12633-017-9660-8.
  • 17. Jailani H.S., Rajadurai A., Mohan B., Kumar A.S., Kumar T.S. Multi-response optimization of sintering parameters if Al-Si alloy/fly ash composite using Taguchi method and grey relational analysis. Int. J. Adv. Manuf. Technol. 2009; 45: 362–369. https://doi.org/10.1007/s00170-009-1973-3.
  • 18. Pillai J.U., Sanghrajka I., Shunmugavel M., Muthuramalingam T., Goldberg M., Littlefair G., Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach. Measurement. 2018; 124: 291–298. https://doi.org/10.1016/j.measurement.2018.04.052.
  • 19. Manoj M., Jinu G.R., Muthuramalingam T. Multi response optimization of AWJM process parameters on machining TiB2 particles reinforced Al7075 composite using Taguchi-DEAR methodology. Silicon. 2018; 10: 2287–2293. https://doi.org/10.1007/s12633-018-9763-x.
  • 20. Ismail M.R.M., Thangaraj M., Karmiris-Obratański P., Papazoglou E., Karkalos N. Design of real-time extremum-seeking controller-based modelling for optimizing MRR in low power EDM. Materials. 2023; 16: 434. https://doi.org/10.3390/ma16010434.
  • 21. Ramamurthy A., Sivaramakrishnan R., Thangaraj M. Taguchi-Grey computation methodology for optimum multiple performance measures on machining titanium alloy in WEDM process. Indian J. Eng. Mater. Sci. 2015; 22: 181–186. http://nopr.niscpr.res.in/handle/123456789/31505.
  • 22. Wang Z., Zhang T., Yu T., Zhao J. Assessment and optimization of grinding process on AISI 1045 steel in terms of green manufacturing using orthogonal experimental design and grey relational analysis. J. Clean. Prod. 2020; 253: 119896. https://doi.org/10.1016/j.jclepro.2019.119896.
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  • 24. Wu Y., Zhou F., Kong J. Innovative design approach for product design based on TRIZ, AD, fuzzy and Grey relational analysis. Comput. Ind. Eng. 2020; 140: 106276. https://doi.org/10.1016/j.cie.2020.106276.
  • 25. Ekmekci B. White layer composition, heat treatment, and crack formation in electric discharge machining process. Metall Mater Trans B 2009; 40: 70–81. https://doi.org/10.1007/s11663-008-9220-0.
  • 26. Muthuramalingam T., Mohan B., Rajadurai A., Prakash M.D.A.A. Experimental investigation of iso energy pulse generator on performance measures in EDM. Mater. Manuf. Process. 2013; 28: 1137–1142. https://doi.org/10.1080/10426914.2013.811749.
  • 27. Muthuramalingam, T. Effect of diluted dielectric medium on spark energy in green EDM process using TGRA approach. J. Clean. Prod. 2019; 238: 117894. https://doi.org/10.1016/j.jclepro.2019.117894.
  • 28. Karmiris-Obratański P., Papazoglou E.L., Leszczyńska-Madej B., Karkalos N.E., Markopoulos A.P. An optimalization study on the surface texture and machining parameters of 60CrMoV18-5 steel by EDM. Materials. 2022; 15: 3559. https://doi.org/10.3390/ma15103559.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-69837bf1-1524-4091-b544-c9c347714da5
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