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Journal of Polymer & Composites Original Research Special issue

An Experimental Study on Multi-Criteria Parameters Optimization of Process for Al6351/SiC/Gr Metal Matrix Composites Using AHP-TOPSIS Approach

  1. Sneha H. Dhoria Department of Mechanical Engineering, Andhra University, Visakhapatnam
  2. K. Venkata Subbaiah Department of Mechanical Engineering, Andhra University, Visakhapatnam
  3. V. Durga Prasada Rao Department of Mechanical Engineering, SRKR Engineering College, Bhimavaram

Abstract

This research focuses on optimizing the process parameters of Wire Electrical Discharge Machining (WEDM) for a hybrid Metal Matrix Composite (MMC) comprising Al6351 aluminum alloy reinforced with 4% SiC and 6% graphite (Gr), fabricated via squeeze casting. This technique enables the formation of dense, defect-free composites with uniform reinforcement distribution, enhancing both mechanical properties and structural integrity. Microstructural characterization using Scanning Electron Microscopy (SEM) confirmed the even dispersion and bonding of reinforcements. The expected phases were confirmed by X-ray diffraction (XRD) analysis, which also confirmed thermal compatibility by removing any undesired intermetallic formations. An L27 orthogonal array was employed to design the WEDM experiments by varying parameters such as pulse-on time, pulse-off time, wire feed, spark voltage, and wire tension. The performance was evaluated based on multiple responses—dimensional deviation (DD), surface roughness (SR), kerf width (K), and material removal rate (MRR).To achieve optimal machining conditions, the study used the AHP-TOPSIS technique. AHP was applied to assign objective weights to each response, while TOPSIS was used to determine the best parameter settings based on closeness coefficients. This hybrid approach effectively handles multi-response optimization and provides a more accurate and systematic alternative to conventional methods.

Keywords

References (31)

  1. Low IM, Dong Y, editors. Composite materials: manufacturing, properties and applications. 1st ed. Amsterdam: Elsevier; 2021. 688 p. ISBN: 9780128205129.
  2. Uthayakumar M, Prabhakaran G, Aravindan S, Sivaprasad JV. Influence of Cutting Force on Bimetallic Piston Machining by a Cubic Boron Nitride (CBN) Tool. Materials and Manufacturing Processes. 2012;27(10):1078-1083. doi:10.1080/10426914.2012.677913
  3. Dhoria, S. H.; Durga Prasada Rao, V.; Venkata Subbaiah, K. Assessment of Mechanical and Tribological Properties of Aluminium Hybrid Metal Matrix Composites: A Review. Research Review International Journal of Multidisciplinary 2019, 4(5), 1049-1056.
  4. Monica V, Lakshmikanth G, Lathicashree S, Senthilkumar N, Muniappan A, Deepanraj B. An experimental analysis and optimization of heat treatment parameters of Al6061 alloy for improved mechanical properties. International Journal of Mechanical and Production Engineering Research and Development. 2019; 9(Special Issue):46-59.
  5. Phate MR, Toney SB, Phate VR. Multi-parametric Optimization of WEDM Using Artificial Neural Network (ANN)-Based PCA for Al/SiCp MMC. Journal of The Institution of Engineers (India): Series C. 2020;102(1):169-181. doi:10.1007/s40032-020-00615-1
  6. Lal K, Trehan R. Review of wire breakage causes and mitigation strategies in wire electrical discharge machining. Machining Science and Technology. 2025;29(2):212-258. doi:10.1080/10910344.2025.2475463
  7. Kumar N, Kumari S, Abhishek K, Nandi G, Ghosh N. Study on various parameters of WEDM using different optimization techniques: A review. Materials Today: Proceedings. 2022;62:4018-4024. doi:10.1016/j.matpr.2022.04.596
  8. Sheth M, Gajjar K, Jain A, Shah V, Patel H, Chaudhari R, et al. Multi-objective Optimization of Inconel 718 Using Combined Approach of Taguchi—Grey Relational Analysis. Lecture Notes in Mechanical Engineering. 2020:229-235. doi:10.1007/978-981-15-3639-7_27
  9. Rathi P, Ghiya R, Shah H, Srivastava P, Patel S, Chaudhari R, et al. Multi-response Optimization of Ni55.8Ti Shape Memory Alloy Using Taguchi–Grey Relational Analysis Approach. Lecture Notes in Intelligent Transportation and Infrastructure. 2019:13-23. doi:10.1007/978-981-32-9971-9_2
  10. Patel S, Fuse K, Gangvekar K, Badheka V. Multi-Response Optimization of Dissimilar Al-Ti Alloy FSW Using Taguchi - Grey Relational Analysis. Key Engineering Materials. 2020;833:35-39. doi:10.4028/www.scientific.net/kem.833.35" rel="nofollow">doi:10.4028/www.scientific.net/kem.833.35
  11. Chaudhari R, Vora JJ, Prabu SSM, Palani IA, Patel VK, Parikh DM. Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm. Advances in Manufacturing. 2019;9(1):64-80. doi:10.1007/s40436-019-00267-0
  12. Vora J, Patel VK, Srinivasan S, Chaudhari R, Pimenov DY, Giasin K, et al. Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies. Metals. 2021;11(6):981. doi:10.3390/met11060981
  13. Chaudhari R, Khanna S, Vora J, Patel VK, Paneliya S, Pimenov DY, et al. Experimental investigations and optimization of MWCNTs-mixed WEDM process parameters of nitinol shape memory alloy. Journal of Materials Research and Technology. 2021;15:2152-2169. doi:10.1016/j.jmrt.2021.09.038
  14. Nain SS, Garg D, Kumar S. Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization. Engineering Science and Technology, an International Journal. 2018;21(2):261-273. doi:10.1016/j.jestch.2018.03.005
  15. Sharma N, Khanna R, Gupta RD. WEDM process variables investigation for HSLA by response surface methodology and genetic algorithm. Engineering Science and Technology, an International Journal. 2015;18(2):171-177. doi:10.1016/j.jestch.2014.11.004
  16. Phate MR, Toney SB. Modeling and prediction of WEDM performance parameters for Al/SiCp MMC using dimensional analysis and artificial neural network. Engineering Science and Technology, an International Journal. 2019;22(2):468-476. doi:10.1016/j.jestch.2018.12.002
  17. Haddad M, Sanders D. Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty. Operations Research Perspectives. 2018 Jan 1;5:357-70.
  18. Vassoney E, Mammoliti Mochet A, Desiderio E, Negro G, Pilloni MG, Comoglio C. Comparing Multi-Criteria Decision-Making Methods for the Assessment of Flow Release Scenarios From Small Hydropower Plants in the Alpine Area. Frontiers in Environmental Science. 2021;9. doi:10.3389/fenvs.2021.635100
  19. Babu RD, Gurusamy P, Herbert Bejaxhin AB, Chandramohan P. Influences of WEDM constraints on tribological and micro structural depictions of SiC-Gr strengthened Al2219 composites. Tribology International. 2023;185:108478. doi:10.1016/j.triboint.2023.108478
  20. Ramraji K, Rajkumar K, Selvakumar G, S RP. Investigations on the influence of particle reinforcement and wire materials on the surface quality and machining characteristics of AA6061-TiB 2 alloy in WEDM. Surface Topography: Metrology and Properties. 2021;9(3):035029. doi:10.1088/2051-672x/ac1f7c
  21. Alaneme KK, Ajayi OJ. Microstructure and mechanical behavior of stir-cast Zn–27Al based composites reinforced with rice husk ash, silicon carbide, and graphite. Journal of King Saud University - Engineering Sciences. 2017;29(2):172-177. doi:10.1016/j.jksues.2015.06.004
  22. Varghese AN, Pradeep PV. Experimental investigation of wear characteristics on AL/SIC/GR composite material. International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME). 2014;3(3).
  23. Jayaganthan A, Manickam M, Prathiban S, Amarnath M, Subramanian K, Babu M, et al. Multiobjective Optimization of WEDM Parameters on the Mg-HNT-Zr Hybrid Metal Matrix Composite Using Taguchi-Coupled GRA. Advances in Materials Science and Engineering. 2022;2022:1-11. doi:10.1155/2022/1920810
  24. Anandan D, Aravind D, Dhanraj K, Abinesh R, Barathkumar P. Performance of wire electrodes on machining of hybrid Aluminium composite. International Journal of Applied Engineering Research.; 10(65):2015.
  25. Soundararajan R, Ramesh A, Mohanraj N, Parthasarathi N. An investigation of material removal rate and surface roughness of squeeze casted A413 alloy on WEDM by multi response optimization using RSM. Journal of Alloys and Compounds. 2016;685:533-545. doi:10.1016/j.jallcom.2016.05.292
  26. Sundram S, Shree Raman M, Balamuralitharan S. Influence of process parameters on machining studies on stir casted MMCs with AA6351 and TiO2 by grey and desirability approaches. Materials Today: Proceedings. 2023;77:551-556. doi:10.1016/j.matpr.2023.01.091
  27. Dhoria SH, Durga Prasada Rao V, Venkata Subbaiah K. Mechanical and wear behaviour of 6351 Al/Gr/SiC composites fabricated by squeeze casting. Materials Today: Proceedings. 2019;18:2107-2113. doi:10.1016/j.matpr.2019.06.326
  28. kumar J, Soota T, Rajput SK. Optimization and measurement of kerf width and surface roughness of AISI 316L. Forces in Mechanics. 2022;6:100071. doi:10.1016/j.finmec.2022.100071
  29. Tosun N, Cogun C, Tosun G. A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method. Journal of Materials Processing Technology. 2004;152(3):316-322. doi:10.1016/j.jmatprotec.2004.04.373
  30. Saaty TL. The analytic hierarchy process mcgraw hill, New York. Agricultural Economics Review. 1980;70(804):10-21236.
  31. Ram Prasad AVS, Ramji K, Kolli M, Vamsi Krishna G. Multi-Response Optimization of Machining Process Parameters for Wire Electrical Discharge Machining of Lead-Induced Ti-6Al-4V Alloy Using AHP–TOPSIS Method. Journal of Advanced Manufacturing Systems. 2019;18(02):213-236. doi:10.1142/s0219686719500112
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