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Microstructure characterization and maximization of the material removal rate in nano-powder mixed EDM of Al-Mg2Si metal matrix composite—ANFIS and RSM approaches

  • Mehdi Hourmand
  • Ahmed A. D. Sarhan
  • Saeed Farahany
  • Mohd Sayuti
ORIGINAL ARTICLE

Abstract

Al-Mg2Si in situ composite is a new metal matrix composite (MMC) with numerous applications in different engineering fields. MMCs are considered difficult-to-cut materials due to the abrasive nature of the reinforcement (e.g., Mg2Si), hardness, and built-up edge. Hence, electrical discharge machining (EDM) is one of the alternative ways to machine Al-Mg2Si. With EDM, it is possible to machine conductive materials with different strength, temperature resistance, and hardness as well as produce complicated shapes, high-aspect ratio slots, and deep cavities with precise dimensions and good surface finish. The experiments in this study were designed by response surface methodology (RSM) and ANFIS was utilized to analyze the nano-powder mixed EDM (NPMEDM) of Al-Mg2Si in situ composite. The study represents the impacts of NPMEDM parameters on changes in microstructure and material removal rate (MRR). The results revealed that among all interactions, the current-voltage and current-pulse ON time interactions have the most significant effect on MRR. Moreover, current has most significant effect, followed by voltage, pulse ON time and duty factor. An analysis of the Al-Mg2Si microstructure demonstrated that current, pulse ON time, and voltage have remarkable impact on the microstructure, size of craters, and profile of the machined surface. Moreover, decrease in spark energy leads to less microstructural change and better surface finish.

Keywords

Al-Mg2Si metal matrix composite (MMC) Nano-powder mixed electrical discharge machining (Nano-powder mixed EDM) Adaptive neuro-fuzzy inference system (ANFIS) Response surface methodology (RSM) Material removal rate (MRR) Microstructure 

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Notes

Acknowledgments

The authors would like to acknowledge both of University of Malaya and King Fahd University of Petroleum & Minerals for providing support.

References

  1. 1.
    Jahangiri A, Idris MH, Farahany S (2013) Investigation on tungsten inert gas welding of in situ Al-15 and 20 Mg2Si composites with an Al-Si filler. J Compos Mater 47(10):1283–1291CrossRefGoogle Scholar
  2. 2.
    Qin Q, Zhao Y, Xiu K, Zhou W, Liang Y (2005) Microstructure evolution of in situ Mg2Si /Al–Si–Cu composite in semisolid remelting processing. Mater Sci Eng A 407(1):196–200CrossRefGoogle Scholar
  3. 3.
    Razavykia A, Farahany S, Yusof NM (2015) Evaluation of cutting force and surface roughness in the dry turning of Al–Mg2Si in-situ metal matrix composite inoculated with bismuth using DOE approach. Measurement 76:170–182CrossRefGoogle Scholar
  4. 4.
    Kılıçkap E, Çakır O, Aksoy M, İnan A (2005) Study of tool wear and surface roughness in machining of homogenised SiC-p reinforced aluminium metal matrix composite. J Mater Process Technol 164-165:862–867CrossRefGoogle Scholar
  5. 5.
    Hourmand M, Sarhan AA, Sayuti M (2017) Micro-electrode fabrication processes for micro-EDM drilling and milling: a state-of-the-art review. Int J Adv Manuf Technol 91(1–4):1023–1056CrossRefGoogle Scholar
  6. 6.
    Flaño O, Ayesta I, Izquierdo B, Sánchez JA, Zhao Y, Kunieda M (2018) Improvement of EDM performance in high-aspect ratio slot machining using multi-holed electrodes. Precis Eng 51:223–231CrossRefGoogle Scholar
  7. 7.
    Nakagawa T, Yuzawa T, Sampei M, Hirata A (2017) Improvement in machining speed with working gap control in EDM milling. Precis Eng 47:303–310CrossRefGoogle Scholar
  8. 8.
    Hourmand M, Sarhan AAD, Noordin MY (2017) Development of new fabrication and measurement techniques of micro-electrodes with high aspect ratio for micro EDM using typical EDM machine. Measurement 97:64–78CrossRefGoogle Scholar
  9. 9.
    Hourmand M, Noordin MY (2014) Micro-electrode fabrication process using EDM. Adv Mater Res 845:980–984Google Scholar
  10. 10.
    Daud ND, AbuZaiter A, Leow PL, Ali MSM (2018) The effects of the silicon wafer resistivity on the performance of microelectrical discharge machining. Int J Adv Manuf Technol 95(1–4):257–266CrossRefGoogle Scholar
  11. 11.
    Mohal S, Kumar H (2017) Parametric optimization of multiwalled carbon nanotube-assisted electric discharge machining of Al-10% SiCp metal matrix composite by response surface methodology. Mater Manuf Process 32(3):263–273CrossRefGoogle Scholar
  12. 12.
    Singh B, Kumar J, Kumar S (2016) Investigation of the tool wear rate in tungsten powder-mixed electric discharge machining of AA6061/10% SiCp composite. Mater Manuf Process 31(4):456–466CrossRefGoogle Scholar
  13. 13.
    Assarzadeh S, Ghoreishi M (2013) A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining (PMEDM) parameters. Int J Adv Manuf Technol 64(9–12):1459–1477CrossRefGoogle Scholar
  14. 14.
    Kung K-Y, Horng J-T, Chiang K-T (2009) Material removal rate and electrode wear ratio study on the powder mixed electrical discharge machining of cobalt-bonded tungsten carbide. Int J Adv Manuf Technol 40(1–2):95–104CrossRefGoogle Scholar
  15. 15.
    Hourmand M, Sarhan AAD, Noordin MY, Sayuti M (2017) 1.10 Micro-EDM drilling of tungsten carbide using microelectrode with high aspect ratio to improve MRR, EWR, and hole quality A2 - Hashmi, MSJ. In: Comprehensive materials finishing. Elsevier, Oxford, pp 267–321Google Scholar
  16. 16.
    Jameson EC (2001) Electrical discharge machining. In: Society of Manufacturing Engineers. Dearbern, MichiganGoogle Scholar
  17. 17.
    Pramanik A (2014) Developments in the non-traditional machining of particle reinforced metal matrix composites. Int J Mach Tools Manuf 86:44–61CrossRefGoogle Scholar
  18. 18.
    Kumar SV, Kumar MP (2015) Machining process parameter and surface integrity in conventional EDM and cryogenic EDM of Al–SiCp MMC. J Manuf Process 20:70–78CrossRefGoogle Scholar
  19. 19.
    Seo Y, Kim D, Ramulu M (2006) Electrical discharge machining of functionally graded 15–35 vol% SiCp/Al composites. Mater Manuf Process 21(5):479–487CrossRefGoogle Scholar
  20. 20.
    Daneshmand S, Masoudi B (2017) Investigation of weight percentage of alumina fiber on EDM of Al/Al2O3 metal matrix composites. Silicon 10(3):1003–1011CrossRefGoogle Scholar
  21. 21.
    Senthilkumar V, Omprakash BU (2011) Effect of titanium carbide particle addition in the aluminium composite on EDM process parameters. J Manuf Process 13(1):60–66CrossRefGoogle Scholar
  22. 22.
    Sidhu SS, Batish A, Kumar S (2013) Study of surface properties in particulate reinforced MMC using powder-mixed EDM. Mater Manuf Process 29(1):46–52CrossRefGoogle Scholar
  23. 23.
    Singh S, Yeh M-F (2012) Optimization of abrasive powder mixed EDM of aluminum matrix composites with multiple responses using gray relational analysis. J Mater Eng Perform 21(4):481–491CrossRefGoogle Scholar
  24. 24.
    Velmurugan C, Subramanian R, Thirugnanam S, Ananadavel B (2011) Experimental investigations on machining characteristics of Al 6061 hybrid metal matrix composites processed by electrical discharge machining. Int J Eng Sci Technol 3(8):87–101Google Scholar
  25. 25.
    Gopalakannan S, Senthilvelan T (2013) A parametric study of electrical discharge machining process parameters on machining of cast Al/B4C metal matrix nanocomposites. Proc Inst Mech Eng B J Eng ManufGoogle Scholar
  26. 26.
    Gopalakannan S, Senthilvelan T (2013) EDM of cast Al/SiC metal matrix nanocomposites by applying response surface method. Int J Adv Manuf Technol 67(1–4):485–493CrossRefGoogle Scholar
  27. 27.
    Kumar R, Singh I, Kumar D (2013) Electro discharge drilling of hybrid MMC. Procedia Eng 64:1337–1343CrossRefGoogle Scholar
  28. 28.
    Çaydaş U, Hasçalık A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst Appl 36(3, Part 2):6135–6139CrossRefGoogle Scholar
  29. 29.
    Yadegaridehkordi E, Hourmand M, Nilashi M, Shuib L, Ahani A, Ibrahim O (2018) Influence of big data adoption on manufacturing companies' performance: an integrated DEMATEL-ANFIS approach. Technol Forecast Soc Chang 137:199–210.  https://doi.org/10.1016/j.techfore.2018.07.043 CrossRefGoogle Scholar
  30. 30.
    Maher I, Sarhan AAD, Marashi H, Barzani MM, Hamdi M (2016) White layer thickness prediction in wire-EDM using CuZn-coated wire electrode—ANFIS modelling. Trans IMF 94(4):204–210CrossRefGoogle Scholar
  31. 31.
    Suganthi XH, Natarajan U, Sathiyamurthy S, Chidambaram K (2013) Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model. Int J Adv Manuf Technol 68(1–4):339–347CrossRefGoogle Scholar
  32. 32.
    Maher I, Eltaib MEH, Sarhan AAD, El-Zahry RM (2015) Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining. Int J Adv Manuf Technol 76(5–8):1459–1467CrossRefGoogle Scholar
  33. 33.
    Aydın M, Karakuzu C, Uçar M, Cengiz A, Çavuşlu MA (2013) Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning. Int J Adv Manuf Technol 67(1–4):957–967CrossRefGoogle Scholar
  34. 34.
    Instruction Manual. Sodick Wire-Cut EDM PGM WHITE 3 AG Series (Version 2.0)Google Scholar
  35. 35.
    Hourmand M, Farahany S, Sarhan AA, Noordin MY (2015) Investigating the electrical discharge machining (EDM) parameter effects on Al-Mg2Si metal matrix composite (MMC) for high material removal rate (MRR) and less EWR–RSM approach. Int J Adv Manuf Technol 77(5–8):831–838CrossRefGoogle Scholar
  36. 36.
    Lo S-P (2003) An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mater Process Technol 142(3):665–675CrossRefGoogle Scholar
  37. 37.
    Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligenceGoogle Scholar
  38. 38.
    Egashira K, Matsugasako A, Tsuchiya H, Miyazaki M (2006) Electrical discharge machining with ultralow discharge energy. Precis Eng 30(4):414–420CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center of Advanced Manufacturing and Materials Processing (AMMP), Department of Mechanical EngineeringUniversity of Malaya (UM)Kuala LumpurMalaysia
  2. 2.Department of Mechanical EngineeringUniversity of Malaya (UM)Kuala LumpurMalaysia
  3. 3.Department of Mechanical EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  4. 4.Department of chemical and Materials EngineeringBuein Zahra Technical UniversityGazvinIran

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