Influence of Hybrid Nanofluid Composition on Surface Roughness and Hardness During Roller Burnishing of Aluminum Alloys
Keywords:
Hybrid Nanofluid, Composition Ratio, Roller Burnishing, Aluminum Alloys, Surface Roughness, MicrohardnessAbstract
Hybrid nanofluids, formed by combining two different nanoparticles in a single base fluid, have gained attention for their enhanced lubrication and heat-transfer capabilities. However, the performance of such fluids is strongly influenced by the nanoparticle composition ratio, while most previous studies have employed a fixed 50:50 mixture. This study investigates the effect of hybrid nanofluid composition on surface roughness (Ra) and microhardness (HV) during roller burnishing of Al6061-T6, Al7075-T6, and Al2024-T3 aluminum alloys. Four hybrid nanofluid systems, namely Al₂O₃-CuO, Al₂O₃-TiO₂, Al₂O₃-graphene, and CuO-MWCNT, were prepared using deionized water at a constant nanoparticle concentration of 1.0 wt.%. Five composition ratios ranging from 100:0 to 0:100 were evaluated under identical burnishing conditions. The results showed that the optimum composition varied with the hybrid pair and generally differed from the commonly used 50:50 ratio. Among the investigated formulations, the Al₂O₃-graphene hybrid achieved the lowest surface roughness (Ra = 0.58 μm on Al7075-T6), while the Al₂O₃-CuO hybrid produced the highest microhardness (156 HV). The optimum compositions were identified as 75:25 for Al₂O₃-CuO, 60:40 for Al₂O₃-TiO₂, 60:40 for Al₂O₃-graphene, and 50:50 for CuO-MWCNT. The findings highlight the importance of composition optimization in improving surface integrity during hybrid nanofluid-assisted roller burnishing.
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[1] Amini, S., Bagheri, A., & Teimouri, R. (2019). How alumina nanoparticles impact surface characteristics of Al7175 in roller burnishing process. Journal of Manufacturing Processes, 39, 78–87. https://doi.org/10.1016/j.jmapro.2019.02.012
[2] Arifuddin, A., Mohammad Redhwan, A. A., Azmi, W. H., & Mohd Zawawi, N. N. (2022). Performance of Al₂O₃/TiO₂ hybrid nano-cutting fluid in MQL turning operation via RSM approach. Lubricants, 10(12), 366. https://doi.org/10.3390/lubricants10120366
[3] Babu, M. N., Anandan, V., Yildırım, Ç. V., Babu, M. D., & Sarıkaya, M. (2022). Investigation of the characteristic properties of graphene-based nanofluid and its effect on the turning performance of Hastelloy C276 alloy. Wear, 510–511, 204495. https://doi.org/10.1016/j.wear.2022.204495
[4] Duc, T. M., Long, T. T., & Chien, T. Q. (2019). Performance evaluation of MQL parameters using Al₂O₃ and MoS₂ nanofluids in hard turning 90CrSi steel. Lubricants, 7(5), 40. https://doi.org/10.3390/lubricants7050040
[5] Edelbi, A., Kumar, R., Sahoo, A. K., & Pandey, A. (2022). Comparative machining performance investigation of dual-nozzle MQL-assisted ZnO and Al₂O₃ nanofluids in face milling of Ti–3Al–2.5V alloys. Arabian Journal for Science and Engineering, 47(9), 11005–11022. https://doi.org/10.1007/s13369-021-06595-3
[6] Haghnazari, S., & Abedini, V. (2021). Effects of hybrid Al₂O₃–CuO nanofluids on surface roughness and machining forces during turning AISI 4340. SN Applied Sciences, 3(1), 81. https://doi.org/10.1007/s42452-020-04088-w
[7] Hamid, K. A., Azmi, W. H., Nabil, M. F., Mamat, R., & Sharma, K. V. (2018). Experimental investigation of thermal conductivity and dynamic viscosity on nanoparticle mixture ratios of TiO₂–SiO₂ nanofluids. International Journal of Heat and Mass Transfer, 116, 1143–1152. https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.087
[8] Ho, W.-H., Tsai, J.-T., & Huang, W.-T. (2024). Research on surface roughness of high-speed milling 7075-T6 aluminum alloy using nanofluid/ultrasonic atomization minimal quantity lubrication system. Science Progress, 107(4), 1–18. https://doi.org/10.1177/00368504241284823
[9] Jamil, M., Khan, A. M., Hegab, H., Sarfraz, S., Sharma, N., Mia, M., Gupta, M. K., Zhao, G. L., & Pruncu, C. I. (2019). Effects of hybrid Al₂O₃–CNT nanofluids and cryogenic cooling on machining of Ti–6Al–4V. International Journal of Advanced Manufacturing Technology, 102, 3895–3909. https://doi.org/10.1007/s00170-019-03485-9
[10] Karthikraja, M., Kalidoss, P., Anbu, S., & Prabakaran, P. (2024). Advancements in turning: Exploring hybrid nanofluids and MQL strategies. Journal of Electronics and Informatics, 6(4), 301–316. https://doi.org/10.36548/jei.2024.4.002
[11] Makhesana, M. A., Patel, K. M., & Bagga, P. J. (2022). Evaluation of surface roughness, tool wear and chip morphology during machining of nickel-based alloy under sustainable hybrid nanofluid-MQL strategy. Lubricants, 10(12), 315. https://doi.org/10.3390/lubricants10120315
[12] Nguyen, T.-T., Cao, L.-H., Nguyen, T.-A., & Dang, X.-P. (2020). Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality. Journal of Cleaner Production, 245, 119328. https://doi.org/10.1016/j.jclepro.2019.119328
[13] Patel, K. A., & Brahmbhatt, P. K. (2016). Implementation of Taguchi method in the optimization of roller burnishing process parameter for surface roughness. In Proceedings of the First International Conference on Information and Communication Technology for Intelligent Systems (Vol. 2, pp. 185–195). Springer. https://doi.org/10.1007/978-3-319-30933-0_19
[14] Safiei, W., Rahman, M. M., Yusoff, A. R., Radhwan, H., Tajul Arifin, A. M., & Awang, M. M. R. (2021). Effects of SiO₂-Al₂O₃-ZrO₂ tri-hybrid nanofluids on surface roughness and cutting temperature in end milling of aluminum alloy 6061-T6 using uncoated and coated cutting inserts with minimal quantity lubricant method. Arabian Journal for Science and Engineering, 46(8), 7943–7961. https://doi.org/10.1007/s13369-021-05533-7
[15] Sairaman, S. R., Selvaraj, R., Anbu, V., Karthikeyan, R., & Kumar, J. P. (2025). Sol–gel and co-precipitation synthesized hybrid nanofluids for enhanced CNC turning of AISI 4340 steel: An experimental and machine learning approach. Scientific Reports, 15, 39512. https://doi.org/10.1038/s41598-025-25102-4
[16] Singh, A., Patel, R., & Sharma, P. (2026). Experimental and machine learning evaluation of Al₂O₃ nanofluid lubricants for surface roughness reduction and thermal conductivity enhancement in superfinishing. International Journal of Advanced Manufacturing Technology, 136, 1041–1058. https://doi.org/10.1007/s00170-026-17677-7
[17] Somatkar, A., Dwivedi, R., & Chinchanikar, S. (2024). Optimizing roller burnishing of aluminum alloy 6061-T6: Comparative analysis of dry and lubricated conditions for enhanced surface quality and mechanical properties. Journal of Manufacturing and Materials Processing, 9(11), 360. https://doi.org/10.3390/jmmp9110360
[18] Sundar, L. S., Chandra Mouli, K. V. V., & Said, Z. (2024). Experimental measurement of thermal conductivity and viscosity of Al₂O₃–GO (80:20) hybrid and mono nanofluids: A new correlation. Materials Science and Engineering: B, 305, 117437. https://doi.org/10.1016/j.mseb.2024.117437
[19] Tiwari, A., Agarwal, D., & Singh, A. (2021). Computational analysis of machining characteristics of surface using varying concentration of nanofluids (Al₂O₃, CuO and TiO₂) with MQL. Materials Today: Proceedings, 42(Part 2), 1262–1269. https://doi.org/10.1016/j.matpr.2020.12.950
[20] Yildırım, Ç. V., Sarıkaya, M., Kıvak, T., & Şirin, Ş. (2021). Tribology and machinability performance of hybrid Al₂O₃–MWCNTs nanofluids-assisted MQL for milling Ti-6Al-4V. International Journal of Advanced Manufacturing Technology, 117, 2007–2024. https://doi.org/10.1007/s00170-021-08279-6
[21] Sudake, M. S., Pujari, A. A., Somatkar, A. A., & Chahare, V. V. (2026). Surface roughness optimization in hard turning of AISI D2 steel using RSM and machine learning. International Research Journal of Innovation in Science and Technology, 1(2), 8–15.
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