Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00034 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/bioconf/20249700034 | |
| Published online | 05 April 2024 | |
- M. K. Das, K. Kumar, T. Kr. Barman, and P. Sahoo, “Optimization of Surface Roughness and MRR in Electrochemical Machining of EN31 Tool Steel Using Grey-taguchi Approach,” Procedia Materials Science, vol. 6, pp. 729–740, 2014, DOI: 10.1016/j.mspro.2014.07.089. [CrossRef] [Google Scholar]
- N. S. Khundrakpam, G. S. Brar, and M. B. Devi, “Optimizing the process parameters of ECM using Taguchi method,” in Materials Today: Proceedings, Elsevier Ltd, 2019, pp. 1373–1379. DOI: 10.1016/j.matpr.2020.02.278. [Google Scholar]
- J. Jeykrishnan, B. Vijaya Ramnath, C. Elanchezhian, and S. Akilesh, “Optimization of process parameters in Electro-chemical machining (ECM) of D3 die steels using Taguchi technique,” in Materials Today: Proceedings, Elsevier Ltd, 2017, pp. 7884–7891. DOI: 10.1016/j.matpr.2017.07.124. [CrossRef] [Google Scholar]
- S. kariem Shather and H. Husien Alwan, “Influence of Currents Density and Gap Size on Metal Removal Rate and Surface Roughness in Electrochemical Machining (ECM),” 2012. [Online]. Available: www.pdffactory.com [Google Scholar]
- S. Kariem Shather and B. Ayad Ahmed, “Experimental Investigation to Improve Metal Removal Rate (MRR) and Surface Roughness in Electrochemical Machining,” 2012. [Online]. Available: www.pdffactory.com [Google Scholar]
- X. Chen, Z. Xu, D. Zhu, Z. Fang, and D. Zhu, “Experimental research on electrochemical machining of titanium alloy Ti60 for a blisk,” Chinese Journal of Aeronautics, vol. 29, no. 1. Chinese Journal of Aeronautics, pp. 274–282, Feb. 01, 2016. DOI: 10.1016/j.cja.2015.09.010. [CrossRef] [Google Scholar]
- H. Qasim and S. Aghdeab, “Effect of Potassium Chloride and Potassium Sulphate Electrolyte Solutionon Surface Roughnessand Material Removal Rate in Electro Chemical Machining (ECM),” Engineering and Technology Journal, vol. 37, no. 8A, pp. 341–347, Aug. 2019, DOI: 10.30684/etj.37.8a.5. [CrossRef] [Google Scholar]
- Z. Xu, J. Liu, D. Zhu, N. Qu, X. Wu, and X. Chen, “Electrochemical machining of burn-resistant Ti40 alloy,” Chinese Journal of Aeronautics, vol. 28, no. 4, pp. 1263–1272, Aug. 2015, DOI: 10.1016/j.cja.2015.05.007. [CrossRef] [Google Scholar]
- M. V. A. Ramakrishna and S. Venugopal Rao, “Fabrication of ECM and study of its parameters in NaCl electrolyte,” in Materials Today: Proceedings, Elsevier Ltd, 2021, pp. 934–939. DOI: 10.1016/j.matpr.2021.01.181. [CrossRef] [Google Scholar]
- S. Ajith Arul Daniel, S. Vijay Ananth, A. Parthiban, and S. Sivaganesan, “Optimization of machining parameters in electro chemical machining of Al5059/SiC/MoS2 composites using taguchi method,” in Materials Today: Proceedings, Elsevier Ltd, 2020, pp. 738–743. DOI: 10.1016/j.matpr.2019.06.750. [CrossRef] [Google Scholar]
- B. Gugulothu, P. S. Satheesh Kumar, B. Srinivas, A. Ramakrishna, and S. Vijayakumar, “Investigating the Material Removal Rate Parameters in ECM for Al 5086 Alloy-Reinforced Silicon Carbide/Flyash Hybrid Composites by Using Minitab-18,” Advances in Materials Science and Engineering, vol. 2021, 2021, DOI: 10.1155/2021/2079811. [CrossRef] [Google Scholar]
- N. Abd Al-Hassan, S. H. Aghdeab, and A. F. Ibrahim, “Optimization of Electrochemical Machining Process Based on Artificial Neural Network Technique,” Engineering and Technology Journal, vol. 34, no. 15, pp. 2960–2970, Dec. 2016, DOI: 10.30684/etj.34.15a.16. [CrossRef] [Google Scholar]
- S. Dewangan, “EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS & MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 825,” 2014. [Google Scholar]
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