195
Views
0
CrossRef citations to date
0
Altmetric
Production and Manufacturing

Development of forecast models on electrical discharge machined graphene nanoplatelets reinforced aluminum composite fabricated via stir casting route

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Article: 2328821 | Received 09 Feb 2024, Accepted 05 Mar 2024, Published online: 18 Mar 2024

References

  • Ammisetty, S., Ammisetti, D., Satyanarayana, K., Chitturi, S., & Naik, N. S. (2021). Optimization of EDM process parameters on machining characteristics of sic and graphene reinforced Al 6061-T6nano-Composites. IOP Conference Series: Materials Science and Engineering, 1112(1), 012017. https://doi.org/10.1088/1757-899X/1112/1/012017
  • Balaji, S., Sivakandhan, C., Maniarasan, P., Deepak, D., Senthamarai, K., & Alagarsamy, S. V. (2023). Effect of process parameters on machining behaviour using S/N ratio and ANOVA analysis. Materials Today: Proceedings, 74, 97–104. https://doi.org/10.1016/j.matpr.2022.12.126
  • Bédard, F., Jahazi, M., & Songmene, V. (2020). Die-sinking EDM of Al6061-T6: interactions between process parameters, process performance, and surface characteristics. The International Journal of Advanced Manufacturing Technology, 107(1-2), 333–342. https://doi.org/10.1007/s00170-020-05109-z
  • Bharti, P. S. (2020). Two-step optimization of electric discharge machining using neural network based approach and TOPSIS. Journal of Interdisciplinary Mathematics, 23(1), 81–96. https://doi.org/10.1080/09720502.2020.1741222
  • Bhat, A., Budholiya, S., Aravind Raj, S., Sultan, M. T. H., Hui, D., Md Shah, A. U., & Safri, S. N. A. (2021). Review on nanocomposites based on aerospace applications. Nanotechnology Reviews, 10(1), 237–253. https://doi.org/10.1515/ntrev-2021-0018
  • Bisht, A., Srivastava, M., Kumar, R. M., Lahiri, I., & Lahiri, D. (2017). Strengthening mechanism in graphene nanoplatelets reinforced aluminum composite fabricated through spark plasma sintering. Materials Science and Engineering: A, 695, 20–28. https://doi.org/10.1016/j.msea.2017.04.009
  • Çakiroğlu, R. (2022). Analysis of EDM machining parameters for keyway on Ti-6Al-4V alloy and modelling by artificial neural network and regression analysis methods. Sādhanā, 47(3), 150. https://doi.org/10.1007/s12046-022-01926-y
  • Chakraborty, S., Dey, V., & Ghosh, S. K. (2015). A review on the use of dielectric fluids and their effects in electrical discharge machining characteristics. Precision Engineering, 40, 1–6. https://doi.org/10.1016/j.precisioneng.2014.11.003
  • Chauhan, P., Saloda, M. A., Nandwana, B. P., & Jindal, S. (2022). Parametric study on stainless steel 316L by die sinking EDM for biomedical application. In Proceedings of the International Conference on Industrial and Manufacturing Systems (CIMS-2020) Optimization in Industrial and Manufacturing Systems and Applications (pp. 215–230). Springer International Publishing.
  • Chawla, K. K., & Chawla, N. (2014). Metal matrix composites: automotive applications. Encyclopedia of Automotive Engineering, https://doi.org/10.1002/9781118354179.auto279
  • Chekuri, R. B. R., Eshwar, D., Kotteda, T. K., & Varma, R. S. (2022). Experimental and thermal investigation on die-sinking EDM using FEM and multi-objective optimization using WOA-CS. Sustainable Energy Technologies and Assessments, 50, 101860. https://doi.org/10.1016/j.seta.2021.101860
  • Debnath, S., Rai, R. N., & Sastry, G. R. K. (2018). A study of multiple regression analysis on die sinking edm machining of ex-situ developed Al-4.5 cu-SiC composite. Materials Today: Proceedings, 5(2), 5195–5201. https://doi.org/10.1016/j.matpr.2017.12.101
  • Garg, P., Jamwal, A., Kumar, D., Sadasivuni, K. K., Hussain, C. M., & Gupta, P. (2019). Advance research progresses in aluminium matrix composites: manufacturing & applications. Journal of Materials Research and Technology, 8(5), 4924–4939. https://doi.org/10.1016/j.jmrt.2019.06.028
  • Ghazanlou, S. I., & Eghbali, B. (2021). Fabrication and characterization of GNPs and CNTs reinforced Al7075 matrix composites through the stir casting process. International Journal of Minerals, Metallurgy and Materials, 28(7), 1204–1214. https://doi.org/10.1007/s12613-020-2101-5
  • Ho, K. H., & Newman, S. T. (2003). State of the art electrical discharge machining (EDM). International Journal of Machine Tools and Manufacture, 43(13), 1287–1300. https://doi.org/10.1016/S0890-6955(03)00162-7
  • Kotteda, T. K., Eshwar, D., Balakrishna, G., Kuchampudi, S. V., Prasad, B. D., & Sadasivam, S. (2022). Experimental Investigation on Metal Matrix Nanocomposite: Aluminium Alloy 6061 and 7075 with SiC and Fly Ash. Journal of Nanomaterials, 2022, 1–14. https://doi.org/10.1155/2022/8368934
  • Kotteda, T. K., Kumar, M., & Kumar, P. (2023). Experimental insights and micrographical investigation on graphene nanoplatelet–reinforced aluminum cast composites. The International Journal of Advanced Manufacturing Technology, (in press). https://doi.org/10.1007/s00170-023-12270-8
  • Kotteda, T. K., Kumar, M., Kumar, P., & Chekuri, R. B. R. (2022). Metal matrix nanocomposites: future scope in the fabrication and machining techniques. The International Journal of Advanced Manufacturing Technology, (in press). https://doi.org/10.1007/s00170-022-09847-0
  • Kumar, M. S., Begum, S. R., & Vasumathi, M. (2019). Influence of stir casting parameters on particle distribution in metal matrix composites using stir casting process. Materials Research Express, 6(10), 1065d4. https://doi.org/10.1088/2053-1591/ab4045
  • Laxman, J., & Raj, K. G. (2015). Mathematical modeling and analysis of EDM process parameters based on Taguchi design of experiments. In Journal of Physics: Conference Series, 662(1), 012025. November)(IOP Publishing. https://doi.org/10.1088/1742-6596/662/1/012025
  • Lee, C. H., & Lai, T. S. (2021). An intelligent system for improving electric discharge machining efficiency using artificial neural network and adaptive control of debris removal operations. IEEE Access. 9, 75302–75312. https://doi.org/10.1109/ACCESS.2021.3080297
  • Lin, Y. C., Yan, B. H., & Huang, F. Y. (2001). Surface improvement using a combination of electrical discharge machining with ball burnish machining based on the Taguchi method. The International Journal of Advanced Manufacturing Technology, 18(9), 673–682. https://doi.org/10.1007/s001700170028
  • Naik, S., Das, S. R., Dhupal, D., & Khatua, A. K. (2022). Electrical discharge machining of engineered Al-22% SiC metal matrix composite: surface roughness analysis, optimization, economic analysis, and sustainability assessment. Process Integration and Optimization for Sustainability, 6(2), 223–251. https://doi.org/10.1007/s41660-021-00207-1
  • Okewu, E., Adewole, P., Misra, S., Maskeliunas, R., & Damasevicius, R. (2021). Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence, 35(13), 983–1021. https://doi.org/10.1080/08839514.2021.1922847
  • Palanisamy, D., Devaraju, A., Manikandan, N., Balasubramanian, K., & Arulkirubakaran, D. (2020). Experimental investigation and optimization of process parameters in EDM of aluminium metal matrix composites. Materials Today: Proceedings, 22, 525–530. https://doi.org/10.1016/j.matpr.2019.08.145
  • Paswan, K., Pramanik, A., Chattopadhyaya, S., & Basak, A. K. (2020). A novel approach towards sustainable electrical discharge machining of metal matrix composites (MMCs). The International Journal of Advanced Manufacturing Technology, 106(3-4), 1477–1486. https://doi.org/10.1007/s00170-019-04816-6
  • Radhika, N., Sudhamshu, A. R., & Chandran, G. K. (2014). Optimization of electrical discharge machining parameters of aluminium hybrid composites using Taguchi method. Journal of Engineering Science and Technology, 9(4), 502–512.
  • Rahman, O. A., Sribalaji, M., Mukherjee, B., Laha, T., & Keshri, A. K. (2018). Synergistic effect of hybrid carbon nanotube and graphene nanoplatelets reinforcement on processing, microstructure, interfacial stress and mechanical properties of Al2O3 nanocomposites. Ceramics International, 44(2), 2109–2122. https://doi.org/10.1016/j.ceramint.2017.10.160
  • Raju, K. S. R., Raju, V. R., Raju, P. R. M., & Ghosal, P. (2015). Launching particle to constant reinforcement ratio as a parameter for improving the nanoreinforcement distribution and tensile strength of aluminum nanometal matrix composites: Paper presented at “International Conference on Advances in Design & Manufacturing” (ICAD&M14), 5–7 December 2014, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India. International Journal of Materials Research, 106(8), 909–914. https://doi.org/10.3139/146.111246
  • Raju, K. S. R., Raju, V. R., Raju, P. R. M., Rajesh, S., & Partha, G. (2016). Enhancement of the mechanical properties of an aluminum metal matrix nanocomposite by the hybridization technique. Journal of Materials Research and Technology, 5(3), 241–249. https://doi.org/10.1016/j.jmrt.2015.11.005
  • Sahoo, A. K., Panda, A., Nayak, B. B., Kumar, R., Das, R. K., & Nayak, R. K. (2021). Machinability model and multi-response optimisation of process parameters through regression and utility concept. International Journal of Process Management and Benchmarking, 11(3), 390–414. https://doi.org/10.1504/IJPMB.2021.115009
  • Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1
  • Sharma, A., Vasudevan, B., Sujith, R., Kotkunde, N., Suresh, K., & Gupta, A. K. (2019). Effect of graphene nanoplatelets on the mechanical properties of aluminium metal matrix composite. Materials Today: Proceedings, 18, 2461–2467. https://doi.org/10.1016/j.matpr.2019.07.095
  • Shyn, C. S., Rajesh, R., & Dev Anand, M. (2023). Optimization-based hybrid intelligent model for decision making on Electrical Discharge Machining (EDM) process of A6061/6%B4C and A6061/9%SiC composite materials. Cybernetics and Systems, 54(6), 836–858. https://doi.org/10.1080/01969722.2022.2110685
  • Singh, D. P., Mishra, S., Yadav, S. K. S., Porwal, R. K., & Singh, V. (2023). Comparative analysis and optimization of thermoelectric machining of alumina and silicon carbide-reinforced aluminum metal matrix composites using different electrodes. Journal of Advanced Manufacturing Systems, 22(02), 373–401. https://doi.org/10.1142/S0219686723500191
  • Singh, S. (2012). Optimization of machining characteristics in electric discharge machining of 6061Al/Al 2 O 3 p/20P composites by grey relational analysis. The International Journal of Advanced Manufacturing Technology, 63(9-12), 1191–1202. https://doi.org/10.1007/s00170-012-3984-8
  • Sita Rama Raju, K., Rama Murthy Raju, P., Rajesh, S., Raju, V. R., & Ghosal, P. (2016). An experimental and micrographical investigation on aluminum nano metal matrix composites. Journal of Composite Materials, 50(26), 3627–3641. https://doi.org/10.1177/0021998315623624
  • Uğur, A., Nas, E., & Gökkaya, H. (2020). Investigation of the machinability of SiC reinforced MMC materials produced by molten metal stirring and conventional casting technique in die-sinking electrical discharge machine. International Journal of Mechanical Sciences, 186, 105875. https://doi.org/10.1016/j.ijmecsci.2020.105875
  • Zhang, D. D., He, X. Y., Liu, Y., Li Gao, Y., & Geng, R. (2022). Nanoparticles reinforced Al-matrix composites fabricated by combination of pre-distribution and deformation: a review. Materials Science and Technology, 38(13), 883–901. https://doi.org/10.1080/02670836.2022.2068272