Development of a Neural Network Model and Taguchi-Based Optimization for Dry Sliding Wear Performance of Al 6065 Alloy Reinforced with Nano SiC and Graphene Nanoplatelets

Abstract:

The Al6065 alloy is widely used in various applications, including tubing for furniture, railway and bus structures, pylons, platforms, and pipelines. However, due to the wear experienced in these applications, it is essential to enhance the wear confrontation of this alloy. To address this issue, this research focuses on reinforcing Al6065 with nanoparticles of silicon carbide and graphene by means of the stir casting method. The wear behaviour of the alloy is studied by varying the rotational speed, load, and composition of reinforcement in the stir casting machine using Taguchi design of experiment. The rate of wear and friction coefficient are measured as responses. The obtained results are then analysed and optimized for the minimum of the output responses using S/N ratio analysis. Further, ANOVA is carried out to determine the influence of each parameter, and a model of the neural network is developed to predict the response. The findings indicate that cumulative the percentage of reinforcement enhances the wear resistance of the alloy. The optimized values of the rotational speed, load, and composition of reinforcement lead to improved wear resistance, with a corresponding decrease in the coefficient of friction. The ANOVA results reveal that the rotational speed and load significantly affect the responses, while the reinforcement composition has a moderate effect. The developed neural network model accurately predicts the response with a high degree of accuracy. The model can be used to optimize the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene, as well as for prediction of the alloy's wear behaviour in various application environments. In conclusion, this research delivers a complete thoughtful of the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene. The findings can be used to optimize the wear resistance and improve the routine of this alloy in various applications.

• Periodical: Materials Science Forum (Volume 1098)
• Pages: 13-27
• DOI: https://doi.org/10.4028/p-Ie2Lu2
• Online since: September 2023
• Authors: V. Pandiaraj*, R. Thiyagarajan, G. Sivasakthi, K. Dhineshkumar, M. Sivanesh, C. Muguthan
• Keywords: Al 6065 Alloy, Artificial Neural Network, Nano Particles, Taguchi

References

[1] F. Gamdani, R. Boukhili, and A. Vadean, "Tensile strength of open-hole, pin-loaded and multi-bolted single-lap joints in woven composite plates," Materials and Design, vol. 88, p.702–712, 2015.

DOI: 10.1016/j.matdes.2015.09.008

[2] N. Sunitha and K. G. Manjunatha, "Evaluation of Corrosion Studies of as Casted and Heat Treated Aluminum 6065 Metal Matrix Composite by Weight Loss Method," Materials Today: Proceedings, vol. 5, no. 10, p.22727–22733, 2018.

DOI: 10.1016/j.matpr.2018.06.651

[3] K. N. S. Kumara et al., "Synthesis and Characterization of Low Cost MgO Nanoparticle for the Assessment of the corrosion performance on Aluminium 6065," Materials Today: Proceedings, vol. 4, no. 11, p.12118–12124, 2017.

DOI: 10.1016/j.matpr.2017.09.139

[4] K. Kondoh, T. Threrujirapapong, H. Imai, J. Umeda, and B. Fugetsu, "CNTs/TiC Reinforced Titanium Matrix Nanocomposites via Powder Metallurgy and Its Microstructural and Mechanical Properties," Journal of Nanomaterials, vol. 2008, p.127538, 2008.

DOI: 10.1155/2008/127538

[5] P. Murugan, K. Naresh, K. Shankar, R. Velmurugan, and G. Balaganesan, "High velocity impact damage investigation of carbon/epoxy/clay nanocomposites using 3D Computed Tomography," Materials Today: Proceedings, vol. 5, no. 9, p.16946–16955, 2018.

DOI: 10.1016/j.matpr.2018.04.098

[6] X. Liao et al., "A highly stretchable and deformation-insensitive bionic electronic exteroceptive neural sensor for human-machine interfaces," Nano Energy, vol. 80, no. August 2020, p.105548, 2021.

DOI: 10.1016/j.nanoen.2020.105548

[7] Y. Peng et al., "Multiscale 3D finite element analysis of aluminum matrix composites with nano&micro hybrid inclusions," Composite Structures, vol. 288, no. November 2021, p.115425, 2022.

DOI: 10.1016/j.compstruct.2022.115425

[8] J. P. Ajithkumar and M. Anthony Xavior, "Cutting force and surface roughness analysis during turning of Al 7075 based hybrid composites," Procedia Manufacturing, vol. 30, p.180–187, 2019.

DOI: 10.1016/j.promfg.2019.02.026

[9] P. Malik, Bhasha, and P. Jain, "Influence of surface modified graphene oxide on mechanical and thermal properties of epoxy resin," Oriental Journal of Chemistry, vol. 34, no. 3, p.1597–1603, 2018.

DOI: 10.13005/ojc/340353

[10] S. Zhang et al., "Graphene/ZrO2/aluminum alloy composite with enhanced strength and ductility fabricated by laser powder bed fusion," Journal of Alloys and Compounds, vol. 910, p.164941, 2022.

DOI: 10.1016/j.jallcom.2022.164941

[11] R. Sankaranarayanan, R. J. H. N., S. K. J., and G. M. Krolczyk, "A comprehensive review on research developments of vegetable-oil based cutting fluids for sustainable machining challenges," Journal of Manufacturing Processes, vol. 67. Elsevier Ltd, p.286–313, Jul. 01, 2021.

DOI: 10.1016/j.jmapro.2021.05.002

[12] P. Naik, D. D. Babu, R. Su, A. El-Shafei, and A. V. Adhikari, "Synthesis, Characterization and Performance Studies of a New Metal-Free Organic Sensitizer for DSSC application," Materials Today: Proceedings, vol. 5, no. 1, p.3150–3157, 2018.

DOI: 10.1016/j.matpr.2018.01.122

[13] T. Maridurai, D. Balaji, and S. Sagadevan, "Synthesis and characterization of yttrium stabilized zirconia nanoparticles," Materials Research, vol. 19, no. 4, p.812–816, 2016.

DOI: 10.1590/1980-5373-MR-2016-0196

[14] P. Kishore, P. M. Kumar, and D. Dinesh, "Wear analysis of Al 5052 alloy with varying percentage of tungsten carbide," AIP Conference Proceedings, vol. 2128, no. 1, p.040003, Jul. 2019.

DOI: 10.1063/1.5117965

[15] K. Saravanan, J. Francis Xavier, M. P. Sudeshkumar, T. Maridurai, V. Suyamburajan, and V. Jayaseelan, "Optimization of SiC Abrasive Parameters on Machining of Ti-6Al-4V Alloy in AJM Using Taguchi-Grey Relational Method," Silicon, vol. 14, no. 3, p.997–1004, 2022.

DOI: 10.1007/s12633-020-00918-z

[16] Y. Sesharao et al., "Optimization on operation parameters in reinforced metal matrix of AA6066 composite with HSS and Cu," Advances in Materials Science and Engineering, vol. 2021, 2021.

DOI: 10.1155/2021/1609769

[17] S. Hanish Anand, N. Venkateshwaran, J. V. Sai Prasanna Kumar, D. Kumar, C. Ramesh Kumar, and T. Maridurai, "Optimization of Aging, Coating Temperature and Reinforcement Ratio on Biosilica Toughened in-situ Al-TiB2 Metal Matrix Composite: a Taguchi Grey Relational Approach," Silicon, vol. 14, no. 8, p.4337–4347, 2022.

DOI: 10.1007/s12633-021-01232-y

[18] T. Panneerselvam, T. K. Kandavel, and P. Kishore, "Experimental Investigation on Cutting Tool Performance of Newly Synthesized P/M Alloy Steel Under Turning Operation," Arabian Journal for Science and Engineering, vol. 44, no. 6, p.5801–5809, Jun. 2019.

DOI: 10.1007/s13369-019-03763-4

[19] N. Mondal, S. Mandal, and M. C. Mandal, "FPA based optimization of drilling burr using regression analysis and ANN model," Measurement: Journal of the International Measurement Confederation, vol. 152, Feb. 2020.

DOI: 10.1016/j.measurement.2019.107327

[20] M. Hanief, M. F. Wani, and M. S. Charoo, "Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis," Engineering Science and Technology, an International Journal, vol. 20, no. 3, p.1220–1226, 2017.

DOI: 10.1016/j.jestch.2016.10.019

[21] N. C. Sattaru, M. R. Baker, D. Umrao, U. K. Pandey, M. Tiwari, and M. K. Chakravarthi, "Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, p.680–683, 2022.

DOI: 10.1109/ICACITE53722.2022.9823697