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Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid

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Abstract

Mixing powder into dielectric fluid in electrical discharge machining (PMEDM) is a very interesting technological solution in current research. This method has the highest efficiency in simultaneously improving the productivity and quality of a machined surface. In this study, material removal rate (MRR), surface roughness (SR), and the micro-hardness of a machined surface (HV) in electrical discharge machining of die steels in dielectric fluid with mixed powder were optimized simultaneously using the Taguchi–TOPSIS method. The process parameters used in the study included workpiece materials (SKD61, SKD11, SKT4), electrode materials (copper, graphite), electrode polarity, pulse-on time, pulse-off time, current, and titanium powder concentration. Some interaction pairs among the process parameters were also used to evaluate the effect on the optimal results. The results showed that MRR and HV increased and SR decreased when Ti powder was mixed into the dielectric fluid in EDM. Factors such as powder concentration, electrode material, electrode polarity, and pulse-off time were found to be significant in the optimal indicator (C*) and the S/N ratio of C*. Powder concentration was also found to be the most significant factor; its contribution to C* was 50.90%, and S/N ratio of C* was 51.46%. The interactions of the powder concentration and certain process parameters for C* were found to be largest. The optimum quality characteristics were MRR = 38.79 mm3/min, SR = 2.71 μm, and HV = 771 HV. The optimal parameters were verified by experiment, and its accuracy was good (max error ≈13.38%). The finished machined surface under optimum conditions was also analyzed. The machined surface quality under optimum conditions was good. In addition, the results of the study showed the TOPSIS limitations of TOPSIS in a multi-criteria optimization problem.

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References

  1. Anirban B, Ajay B (2012) Effect of process variables on microhardness, grain size and strain during machining of various die steels with powder-mixed electric-discharge machining using dummy treated experimental design. Journal of Engineering Manufacture 226:1192–1204

    Article  Google Scholar 

  2. Gangadharudu T, Soumya G (2016) Effect of impregnated powder materials on surface integrity aspects of Inconel 625 during electrical discharge machining. Journal of Engineering Manufacture:1–14

  3. Mai C, Hocheng H, Huang S (2012) Advantages of carbon nanotubes in electrical discharge machining. Int J Adv Manuf Technol 59:111–117

    Article  Google Scholar 

  4. Ramesh S, Jenarthanan MP, Bhuvanesh KAS (2018) Experimental investigation of powder mixed electric dischargemachining of AISI P20 steel using different powders and toolmaterials. Multidiscip Model Mater Struct. https://doi.org/10.1108/MMMS-04-2017-0025

  5. Gangadharudu T, Gangopadhayay S, Biswas CK (2017) State of the art in powder-mixed electric discharge machining: a review. Journal of Engineering Manufacture 231:2511–2526

    Article  Google Scholar 

  6. Mohanty S, Mishra A, Nanda BK, Routara BC (2017) Multi-objective parametric optimization of nano powder mixed electrical discharge machining of AlSiCp using response surface methodology and particle swarm optimization. Alexandria Engineering Journal:3–11. https://doi.org/10.1016/j.aej.2017.02.006

  7. Anderson M, Fred LA, Paulo CSJ, Tiago C (2016) Surface modification of AISI H13 tool steel with silicon or manganese powders mixed to the dielectric in electrical discharge machining process. Int J Adv Manuf Technol 83:1057–1068

    Article  Google Scholar 

  8. Fred LA, Vitor AD, Paulo S, Luciano AM (2017) Surface modification of tool steel by electrical discharge machining with molybdenum powder mixed in dielectric fluid. Int J Adv Manuf Technol 91:341–350

    Article  Google Scholar 

  9. Harmesh K (2015) Development of mirror like surface characteristics using nano powder mixed electric discharge machining. Int J Adv Manuf Technol 76:105–113

    Article  Google Scholar 

  10. Murahari K, Adepu K (2015) Surfactant and graphite powder–assisted electrical discharge machining of titanium alloy. Journal of Engineering Manufacture 231:641–657

    Google Scholar 

  11. Reddy VV, Valli PM, Kumar A (2014) Multi-objective optimization of electrical discharge machining of PH17-4 stainless steel with surfactant-mixed and graphite powder–mixed dielectric using Taguchi-data envelopment analysis–based ranking method. Journal of Engineering Manufacture 229:487–494

    Article  Google Scholar 

  12. Bhattacharya A, Batish A, Singh G (2011) Optimization of powder mixed electric discharge machining using dummy treated experimental design with analytic hierarchy process. Journal of Engineering Manufacture 226:103–116

    Article  Google Scholar 

  13. Assarzadeh S, Ghoreishi M (2013) A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining parameters. Int J Adv Manuf Technol 64:1459–1477

    Article  Google Scholar 

  14. Vijay KM, Man SA (2017) Micro-EDM multiple parameter optimization for Cp titanium. Int J Adv Manuf Technol 89:897–904

    Article  Google Scholar 

  15. Durairaj M, Sudharsun D, Swamynathan N (2013) Analysis of process parameters in wire EDM with stainless steel using single objective Taguchi method and multi objective grey relational grade. Procedia Engineering 64:868–877

    Article  Google Scholar 

  16. Tripathy S, Tripathy DK (2017) Surface characterization and multi-response optimization of EDM process parameters using powder mixed dielectric. Materials Today: Proceedings 4:2058–2067

    Article  Google Scholar 

  17. Rajiv KS, Jagdeep S (2014) Determination of multi-performance characteristics for powder mixed electric discharge machining of tungsten carbide alloy. Journal of Engineering Manufacture 230:303–312

    Google Scholar 

  18. Pragadish N, Kumar MP (2016) Optimization of dry EDM process parameters using grey relational analysis. Arab J Sci Eng 41:4383–4390

    Article  Google Scholar 

  19. Tripathy S, Tripathy DK (2016) Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis. Engineering Science and Technology, an International Journal 19:62–70

    Article  Google Scholar 

  20. Tripathy S, Tripathy DK (2017) Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis. Engineering Science and Technology, an International Journal 21:362–384

    Google Scholar 

  21. Bülent E, Fevzi U, Nihal E (2014) Suspended SiC particle deposition on plastic mold steel surfaces in powder-mixed electrical discharge machining. Journal of Engineering Manufacture 229:475–486

    Google Scholar 

  22. Mohammadreza S, Behnam K (2017) Investigation of carbon nanotube added dielectric on the surface characteristics and machining performance of Ti–6Al–4V alloy in EDM process. J Manuf Process 25:212–219

    Article  Google Scholar 

  23. Behnam K, Mohammadreza S (2017) Effects of hybrid electrical discharge machining processes on surface integrity and residual stresses of Ti-6Al-4V titanium alloy. Int J Adv Manuf Technol 93:1999–2011

    Article  Google Scholar 

  24. Dastagiri M, Rao PS, Valli PM (2016) TOPSIS, GRA methods for parametric optimization on wire electrical discharge machining (WEDM) process. All India Manufacturing Technology, Design and Research Conference

  25. Prabhu S, Vinayagam BK (2016) Multiresponse optimization of EDM process with nanofluids using TOPSIS method and genetic algorithm. Archive of Mechanical Engineering 63:45–71

    Article  Google Scholar 

  26. Gadakh VS (2012) Parametric optimization of wire electrical discharge machining using TOPSIS method. Advances in Production Engineering & Management 7:157–164

    Article  Google Scholar 

  27. Manivannan R, Kumar MP (2017) Multi-attribute decision-making of cryogenically cooled micro-EDM drilling process parameters using TOPSIS method. Journal Materials and Manufacturing Processes 32:209–215

    Article  Google Scholar 

  28. Rajesh K, Anish K, Mohinder PG (2015) Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory. Journal of Industrial Engineering International 11:459–472

    Article  Google Scholar 

  29. Manivannan R, Kumar MP (2016) Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS. J Mech Sci Technol 30:137–144

    Article  Google Scholar 

  30. Atul S, Pankaj A, Rana RS (2017) Applications of TOPSIS algorithm on various manufacturing processes: a review. Materials Today: Proceedings 4:5320–5329

    Article  Google Scholar 

  31. Roy R (1990) A primer on the Taguchi method. Van Nostrand Reinhold, New York

    MATH  Google Scholar 

  32. Naveen B, Harish P, Anil K (2012) To study the effect of polarity and current during electric discharge machining of Inconel 718 with CuW powder metallurgy electrode, Proceedings of the National Conference on Trends and Advances in Mechanical Engineering: 476–481

  33. Satish K, Ashwani KD, Sanjeev K (2017) Parametric optimization of powder mixed electrical discharge machining for nickel-based superalloy inconel-800 using response surface methodology. Mechanics of Advanced Materials and Modern Processes:2–17

  34. Çoğun C, Özerkan KT (2006) An experimental investigation on the effect of powder mixed dielectric on machining performance in electric discharge machining. Journal of Engineering Manufacture 20:1035–1050

    Google Scholar 

Download references

Funding

This research is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number “107.01-2017.303.”

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Correspondence to Huu-Phan Nguyen.

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Nguyen, HP., Pham, VD. & Ngo, NV. Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid. Int J Adv Manuf Technol 98, 1179–1198 (2018). https://doi.org/10.1007/s00170-018-2321-2

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  • DOI: https://doi.org/10.1007/s00170-018-2321-2

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