Wire Electric Discharge Machining of Aluminium Hybrid Composite: Renewable Energy Based IoT Approach

Sreeram Hariharan; Uthayakumar Marimuthu; Thirumalaikumaran Sundaresan; Suresh Kumar Shanmugam; Rajeshkanna Govindhan Radhakrishnan; Darius Mierzwinshi; Janusz Walter

doi:10.59796/jcst.V14N1.2024.12

Authors

Sreeram Hariharan Department of Mechanical Engineering, Kalasalingam Academy of Research & Education, Krishnan Koil 626126, Tamil Nadu, India
Uthayakumar Marimuthu Department of Mechanical Engineering, Kalasalingam Academy of Research & Education, Krishnan Koil 626126, Tamil Nadu, India
Thirumalaikumaran Sundaresan Department of Mechanical Engineering, PSG Institute of Technology and Applied Research, Coimbatore 641062, Tamil Nadu, India
Suresh Kumar Shanmugam Department of Mechanical Engineering, Kalasalingam Academy of Research & Education, Krishnan Koil 626126, Tamil Nadu, India
Rajeshkanna Govindhan Radhakrishnan Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research & Education, Krishnan Koil 626126, Tamil Nadu, India
Darius Mierzwinshi Faculty Material Engineering and Physics, Cracow University of Technology, 30010, Poland
Janusz Walter Faculty Material Engineering and Physics, Cracow University of Technology, 30010, Poland

DOI:

https://doi.org/10.59796/jcst.V14N1.2024.12

Keywords:

Wire electric discharge machining, Internet of Things, Hybrid composite, Renewable energy

Abstract

Wire Electric Discharge Machining (WEDM) has been recognized as one of the optimum methods for machining of harder aluminum-based hybrid metal matrix composites (AHMMC). This method is used to optimize the major control aspects of a machine and they are current, pulse duration, and rate of feed of wire on kerf width (KRW) and Surface roughness (Ra) of hybrid composites made of aluminum Al6351 as the metal matrix (AMMHC). The AMMHC has been created via a stir casting technique by adding SiC and B₄C with an Al6351 matrix. Box-Behnken design (BBD) has been used to conduct tests in order to parametrically optimize the WEDM process. The optimization of KRW and Ra is identified using 3-D surface plots, graphs and response table of ANOVA as well as by employing Response Surface Methodology (RSM). Internet of Things (IoT) is implemented to monitor the quality of electrolyte that is used in WEDM. It has been identified that the current has a major contribution in both KRW and Ra factors. A lower current is preferred for a lower KRW whereas higher current improves R_a value.

References

Akhtar, M., & Khajuria, A. (2023). The Synergistic effects of Boron and Impression Creep Testing during Paced Controlling of Temperature for P91 Steels. Advanced Engineering Materials, 25(16), Article 2300053. https://doi.org/10.1002/adem.202300053

Badizi, R. M., Parizad, A., Askari-Paykani, M., & Shahverdi, H. R. (2020). Optimization of mechanical properties using D-optimal factorial design of experiment: Electromagnetic stir casting process of A357− SiC nanocomposite. Transactions of Nonferrous Metals Society of China, 30(5), 1183-1194. https://doi.org/10.1016/S1003-6326(20)65288-8

Boutrih, L., Makich, H., Nouari, M., & Ayed, L. B. (2022). Surface quality in dry machining of CFRP composite/Ti6Al4V stack laminate. Procedia CIRP, 108, 758–763. https://doi.org/10.1016/j.procir.2022.03.117

Calam, T. T., & Çakıcı, G. T. (2022). Optimization of square wave voltammetry parameters by response surface methodology for the determination of Sunset yellow using an electrochemical sensor based on Purpald®. Food Chemistry, 404(a), Article 134412. https://doi.org/10.1016/j.foodchem.2022.134412

Cui, G., Meng, L., & Zhai, X. (2022). Buckling behaviors of aluminum foam-filled aluminum alloy composite columns under axial compression. Thin-Walled Structures, 177, Article 109399. https://doi.org/10.1016/j.tws.2022.109399

Dhadda, G., Hamed, M., & Koshy, P. (2021). Electrical discharge surface texturing for enhanced pool boiling heat transfer. Journal of Materials Processing Technology, 293, Article 117083. https://doi.org/10.1016/j.jmatprotec.2021.117083

Erol, M., Kısasöz, A., Yaman, P., Karabeyoğlu, S. S., & Barut, U. (2022). A study on high temperature dry sliding wear of AA7050-T4 and effects of the test temperature on microstructure, corrosion behavior, hardness and electrical conductivity. Materials Today Communications, 31, Article 103410. https://doi.org/10.1016/j.mtcomm.2022.103410

Gautam, N., Goyal, A., Sharma, S. S., Oza, A. D., & Kumar, R. (2022). Study of various optimization techniques for electric discharge machining and electrochemical machining processes. Materials Today: Proceedings, 57, 615–621. https://doi.org/10.1016/j.matpr.2022.02.005

Golshan, A., Ghodsiyeh, D., Gohari, S., Ayob, A., & Baharudin, B. H. T. (2013). Optimization of machining parameters during drilling of 7075 aluminium alloy. Applied Mechanics and Materials, 248, 20-25. https://doi.org/10.4028/www.scientific.net/AMM.248.20

Golshan, A., Gohari, S., & Ayob, A. (2012). Multi-objective optimisation of electrical discharge machining of metal matrix composite Al/SiC using non-dominated sorting genetic algorithm. International Journal of Mechatronics and Manufacturing Systems, 5(5/6), 385–398. https://doi.org/10.1504/IJMMS.2012.049972

Govindarajulu, J. (2021). Comparative regression and neural network modeling of roughness and kerf width in CO2 laser cutting of aluminium. Tehničkivjesnik, 28(5), 1437–1441. https://doi.org/10.17559/TV-20190130153849

Gu, W., Kunieda, M., & Zhao, W. (2023). Measurement of discharge reaction force under different discharge conditions in WEDM using Hopkinson bar method. Precision Engineering, 79, 52-59. https://doi.org/10.1016/j.precisioneng.2022.09.001

Hynes, N. R. J., Sankaranarayanan, R., Sujana, J. A. J., Krolczyk, G. M., & Ene, A. (2022). Decision tree approach based green flow-drilling of hybrid aluminium matrix composites using eco-friendly coolants. Journal of Manufacturing Processes, 80, 178-186. https://doi.org/10.1016/j.jmapro.2022.05.050

Jithin, S., & Joshi, S. S. (2021) Surface topography generation and simulation in electrical discharge texturing: A review. Journal of Materials Processing Technology, 298, Article 117297. https://doi.org/10.1016/j.jmatprotec.2021.117297

Joshi, S., Govindan, P., Malshe, A., & Rajurkar, K. (2011). Experimental characterization of dry EDM performed in a pulsating magnetic field. CIRP Annals, 60(1), 239–242. https://doi.org/10.1016/j.cirp.2011.03.114

Khajuria, A., Akhtar, M., Pandey, M. K., Singh, M. P., Raina, A., Bedi, R., & Singh, B. (2019). Influence of ceramic Al2O3 particulates on performance measures and surface characteristics during sinker EDM of stir cast AMMCs. World Journal of Engineering, 16(4), 526-538. https://doi.org/10.1108/WJE-01-2019-0015

Khajuria, A., Bedi, R. Singh, B. and Akhtar, M. (2018). EDM machinability and parametric optimisation of 2014Al/Al2O3 composite by RSM. International Journal of Machining and Machinability of Materials, 20(6), 536–555. https://doi.org/10.1504/IJMMM.2018.096380

Khazaal, S. M., Nimer, N. S., Szabolcs, S., & Abdulsamad, H. J. (2022). Study of manufacturing and material properties of the hybrid composites with metal matrix as tool materials. Results in Engineering, 16, Article 100647. https://doi.org/10.1016/j.rineng.2022.100647

Kumar, R., Katyal, P., & Mandhania, S. (2022). Grey relational analysis based multiresponse optimization for WEDM of ZE41A magnesium alloy. International Journal of Lightweight Materials and Manufacture, 5(4), 543–554. https://doi.org/10.1016/j.ijlmm.2022.06.003

Liang, Z. L., Yun, T. J., Oh, W. B., Lee, B. R., & Kim, I. S. (2020). A study on MOORA-based Taguchi method for optimization in automated GMA welding process. Materials Today: Proceedings, 22, 1778–1785. https://doi.org/10.1016/j.matpr.2020.03.011

Paulson, D. M., Saif, M., & Zishan, M. (2022). Optimization of wire-EDM process of titanium alloy-Grade 5 using Taguchi’s method and grey relational analysis. Materials Today Proceedings, 72, 144-153. https://doi.org/10.1016/j.matpr.2022.06.376

Peta, K., Bartkowiak, T., Galek, P., & Mendak, M. (2021a). Contact angle analysis of surface topographies created by electric discharge machining. Tribology International, 163, Article 107139. https://doi.org/10.1016/j.triboint.2021.107139

Peta, K., Mendak, M., & Bartkowiak, T. (2021b). Discharge energy as a key contributing factor determining microgeometry of aluminum samples created by electrical discharge machining. Crystals, 11(11), Article 1371. https://doi.org/10.3390/cryst11111371

Roy, N. G., Mondal, D., Dey, P., & Ghosh, M. (2022). Optimization of electrical process parameters of WEDM on ECAP Al7075 alloys considering Radial Overcut (ROC) as output response. Materials Today Proceedings, 62(10), 6004-6008. https://doi.org/10.1016/j.matpr.2022.04.982

Saravanan, M., Ramalingam, D., Manikandan, G., & Kaarthikeyen, R. R. (2012). Multi objective optimisation of drilling parameters using Genetic Algorithm. Procedia Engineering, 38, 197–207. https://doi.org/10.1016/j.proeng.2012.06.027

Singh, H., Singh, K., Vardhan, S., & Mohan, S. (2022). A comprehensive review on the new developments consideration in a stir casting processing of aluminum matrix composites. Materials Today: Proceedings, 60(2), 974-981. https://doi.org/10.1016/j.matpr.2021.12.359

Sudhakar, A. N., Markandeya, R., Srinivasa Rao, B., Pandey, A. K., & Kaushik, D. (2022). Effect of alloying elements on the dry sliding wear behavior of high chromium white cast iron and Ni-hard iron. Materials Today Proceedings, 60(3), 1303–1309. https://doi.org/10.1016/j.matpr.2021.09.295

Tan, Z. Y., Wu, X., Guo, J. W., & Zhu, W. (2022). Toughness mechanism and plastic insensitivity of submicron second phase Ta in a novel Ta–Hf6Ta2O17 composite ceramic. Ceramics International, 49(2), 1932-1939. https://doi.org/10.1016/j.ceramint.2022.09.158

Tata, N., Pacharu, R. K., & Devarakonda, S. K. (2021). Multi response optimization of process parameters in wire-cut EDM on INCONEL 625. Materials Today: Proceedings, 47(19), 6960–6964. https://doi.org/10.1016/j.matpr.2021.05.214

Velavan, K., Palanikumar, K., Dhanush, V., Rajapandiyan, S., Kumar, U. T., & Aishwarya, M. (2023). Corrosion and microstructure studies on magnesium alloy composite reinforced with mSiCp fabricated via powder metallurgy. Materials Today: Proceedings, 72, 2132–2138. https://doi.org/10.1016/j.matpr.2022.08.235

Wang, X., Yao, P., Li, Y., Zhou, H., Xiao, Y., Deng, M., ... & Zhou, P. (2023). Effects of material transfer evolution on tribological behavior in CuCrZr alloy paired with 7075 al alloy under current-carrying. Tribology International, 179, Article 107960. https://doi.org/10.1016/j.triboint.2022.107960

Xie, J., Sun, P., & Yang, D. (2021). Adaptive fuzzy-based composite anti-disturbance control for a class of switched nonlinear systems with unknown backlash-like hysteresis. Journal of the Franklin Institute, 358(10), 5213–5236. https://doi.org/10.1016/j.jfranklin.2021.04.041

Yan, W., Meng, X., Cui, X., Liu, Y., Chen, Q., & Jin, L. (2022). Evaporative cooling performance prediction and multi-objective optimization for hollow fiber membrane module using response surface methodology. Applied Energy, 325, Article 119855. https://doi.org/10.1016/j.apenergy.2022.119855

Zhang, L., Wang, W., Zhou, N., Dong, X., Yuan, F., & Rujie, H. (2022). Low temperature fabrication of Cf/BNi/(Ti 0.2 Zr0.2Hf0.2Nb0.2Ta0.2) C-SiCm high entropy ceramic matrix composite by slurry coating and laminating combined with precursor infiltration and pyrolysis." Journal of the European Ceramic Society, 42(7), 3099–3106. https://doi.org/10.1016/j.jeurceramsoc.2022.02.021

Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., ... & Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment and Health, 1(2), 107–116. https://doi.org/10.1016/j.eehl.2022.06.001