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Optimizing the Wire Electrical Discharge Machining (W-EDM) Technique for Al-l2O3/B4C Composite Materials

International Journal of Mechanical Engineering
© 2024 by SSRG - IJME Journal
Volume 11 Issue 5
Year of Publication : 2024
Authors : Ashwin G. Ghaysundar, Mahendra J. Sable, Dilip M. Patel
10.14445/23488360/IJME-V11I5P104
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How to Cite?

Ashwin G. Ghaysundar, Mahendra J. Sable, Dilip M. Patel, "Optimizing the Wire Electrical Discharge Machining (W-EDM) Technique for Al-l2O3/B4C Composite Materials," SSRG International Journal of Mechanical Engineering, vol. 11,  no. 5, pp. 25-36, 2024. Crossref, https://doi.org/10.14445/23488360/IJME-V11I5P104

Abstract:

The rapid advancement of composite material manufacturing is essential in sustaining market growth across multiple industries. This study investigates the impact of Wire Electrical Discharge Machining (WEDM) of Al-Al2O3-B4C composites, which is affected by process variables such as wire feed rate, sample compositions, input current, and pulse time. The two reinforcement particles (wt% of alumina and boron carbide) and three input parameters, pulse-on time (PON), Wire Feed Rate (WFR), and Input current (Ip), were chosen to demonstrate the effect on the output response of Material Removal Rate (MRR) and Roughness Average (Ra). The Grey Relational Analysis (GRA) method determines hybrid composite materials’ MRR and Ra. The Al-based MMC contains micro particles of alumina (45-micron mesh size, 99.90% purity) and Boron Carbide (50micron mesh size, 99.95% purity). Al2O3 and B4C reinforce Al 6061 at weight percentages of 1, 3, 5%, and 1, 2, and 3%, respectively. The stir casting technique is used for composite preparation because it produces a homogeneous mixture. Based on the experimental findings, augmenting the PON and input current leads to a rise in the MRR, while decreasing the PON time and input current improves surface roughness; thus, PON and Ip are highly influencing parameters for MRR, while surface roughness and wire feed rate are fewer influencing parameters. Surface roughness and MRR were improved by using the parameters obtained by the GRA technique, which included a wire feed rate of 6 m/min, input current of 10 A, PON of 105 µs, and Al-MMC Aluminum 6061 with 1% and 5% by weight of boron carbide and alumina.

Keywords:

Al+Al2O3+B4C, Multi-response optimization, Stir casting, WEDM, Grey relational analysis.

References:

[1] Nagender Kumar Chandla, Suman Kant, and M.M. Goud, “A Review on Mechanical Properties of Stir Cast Al-2024 Metal Matrix Composites,” Advances in Materials and Processing Technologies, vol. 9, no. 3, pp. 948-969, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 
[2] Mangesh R. Phate, Shraddha B. Toney, and Vikas R. Phate, “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, vol. 102, pp. 169-181, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] H. Majumder, and K.P. Maity, “Predictive Analysis on Responses in WEDM of Titanium Grade 6 Using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA),” Silicon, vol. 10, pp. 1763-1776, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] V. Kavimani, K. Soorya Prakash, and Titus Thankachan, “Multi-Objective Optimization in WEDM Process of Graphene-SiC-Magnesium Composite Through Hybrid Techniques,” Measurement, vol. 145, pp. 335-349, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] V. Kavimani et al., “WEDM Parameter Optimization for Silicon@ r-GO/Magneisum Composite Using Taguchi Based GRA Coupled PCA,” Silicon, vol. 12, pp. 1161-1175, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] S.A. Sonawane, and M.L. Kulkarni, “Optimization of Machining Parameters of WEDM for Nimonic-75 Alloy Using Principal Component Analysis Integrated with Taguchi Method,” Journal of King Saud University-Engineering Sciences, vol. 30, no. 3, pp. 250258, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Lokesh Singh, Bharat Singh, and Kuldeep K. Saxena, “Manufacturing Techniques for Metal Matrix Composites (MMC): An Overview,” Advances in Materials and Processing Technologies, vol. 6, no. 2, pp. 441-457, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Adeel Ikram et al., “Parametric Optimization for Surface Roughness, Kerf and MRR in Wire Electrical Discharge Machining (WEDM) Using Taguchi Design of Experiment,” Journal of Mechanical Science and Technology, vol. 27, pp. 2133-2141, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sanjeev Kr. Garg, Alakesh Manna, and Ajai Jain, “An Experimental Investigation for Optimization of WEDM Parameters during Machining of Fabricated Al/ZrO2 (p)-MMC,” Arabian Journal for Science and Engineering, vol. 38, pp. 3471-3483, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[10] A.V.S. Ram Prasad, Koona Ramji, and G.L. Datta, “An Experimental Study of Wire EDM on Ti-6Al-4V Alloy,” Procedia Materials Science, vol. 5, pp. 2567-2576, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ashish Goyal, Huzef Ur Rahman, and S.A.C. Ghani, “Experimental Investigation & Optimisation of Wire Electrical Discharge Machining Process Parameters for Ni49Ti51 Shape Memory Alloy,” Journal of King Saud University-Engineering Sciences, vol. 33, no. 2, pp. 129-135, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] M. Ravikumar, H.N. Reddappa, and R. Suresh, “Study on Mechanical and Tribological Characterization of Al2O3/SiCp Reinforced Aluminum Metal Matrix Composite,” Silicon, vol. 10, pp. 2535-2545, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Pankaj K. Gupta, and M.K. Gupta, “Mechanical and Microstructural Analysis of Al-Al2O3/B4C Hybrid Composites,” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 234, no. 12, pp. 1503-1514, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] A.G. Rao et al., “Fabrication and Characterization of Aluminum (6061)-Boron-Carbide Functionally Gradient Material,” Materials and Manufacturing Processes, vol. 25, no. 7, pp. 572-576, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[15] R. Venkata Rao, Advanced Modeling and Optimization of Manufacturing Processes, International Research and Development, 1st ed., Springer London, pp. 1-380, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[16] M. Manjaiah, S. Narendranath, and S. Basavarajappa, “Wire Electro Discharge Machining Performance of TiNiCu Shape Memory Alloy,” Silicon, vol. 8, pp. 467-475, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] P.M. Gopal, K. Soorya Prakash, and S. Jayaraj, “WEDM of Mg/CRT/BN Composites: Effect of Materials and Machining Parameters,” Materials and Manufacturing Processes, vol. 33, no. 1, pp. 77-84, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hargovind Soni, Narendranath Sannayellappa, and Ramesh Motagondanahalli Rangarasaiah, “An Experimental Study of Influence of Wire Electro Discharge Machining Parameters on Surface Integrity of TiNiCo Shape Memory Alloy,” Journal of Material Research, vol. 32, no. 16, pp. 3100-3108, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] T. Mythili, and R. Thanigaivelan, “Optimization of Wire EDM Process Parameters on Al6061/Al2O3 Composite and its Surface Integrity Studies,” Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 68, no. 6, pp. 1403-1412, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] R.V. Rao, V.J. Savsani, and D.P. Vakharia, “Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shipra Verma, and P. Sudhakar Rao, “Study on Mechanical Behavior of Aluminum Alloy 6061 Based Composites A Review,” IOSR Journal of Mechanical and Civil Engineering, vol. 15, no. 4, pp. 16-20, 2018.
[Google Scholar] [Publisher Link]
[22] Yunn-Shiuan Liao, Tzung-Jen Chuang, and Young-Ping Yu, “Study of Machining Parameters Optimization for Different Materials in WEDM,” The International Journal of Advanced Manufacturing Technology, vol. 70, pp. 2051-2058, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Debabrata Rath et al., “WEDM Process Optimization for Machining Characteristics of AISI 52100 Grade Alloy Steel,” Advances in Materials Science and Engineering, vol. 2022, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Ravinder Kumar, and Santram Chauhan, “Study on Surface Roughness Measurement for Turning of Al 7075/10/SiCp and Al 7075 Hybrid Composites by Using Response Surface Methodology (RSM) and Artificial Neural Networking (ANN),” Measurement, vol. 65, pp. 166-180, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ayan Pramanick et al., “Optimization of Wire Electrical Discharge Machining Parameters for Cutting Electrically Conductive Boron Carbide,” Ceramics International, vol. 42, no. 14, pp. 15671-15678, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[26] V. Suresh, A. Daniel Praneet, and J. Anoop, “Ingenious Analysis on Machining Parameters of Aluminium Alloy (LM25)/Graphite (Gr)/Boron Carbide (B4C) Hybrid Composites Using Wire Electrical Discharge Machining (WEDM),” Materials Today: Proceedings, vol. 37, pp. 3112-3117, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] J. Udaya Prakash et al., “Multi-objective Drilling Parameter Optimization of Hybrid Metal Matrix Composites Using Grey Relational Analysis,” Materials Today: Proceedings, vol. 39, pp. 1345-1350, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] J. Udaya Prakash et al., “Multi-Objective Optimization Using Grey Relational Analysis for Wire EDM of Aluminium Matrix Composites,” Materials Today: Proceedings, vol. 72, pp. 2395-2401, 2023.
[CrossRef] [Google Scholar] [Publisher Link]