Sustainable Nano-assisted Dry Turning of Hybrid Aluminium Matrix Composites: Process Parameter Optimisation via Grey Relational Analysis
J. Environ. Nanotechnol., Volume 14, No 3 (September 2025) pp. 655-664
Abstract
The sustainable machining issue has received additional consideration in recent years due to its social, economic, and environmental implications. The paper examines the performance optimisation of green nano-aided dry turning of the hybrid metal matrix aluminium alloy through the lens of Grey Relational Analysis (GRA). By adding a nanoparticle that minimizes a negative environmental impact, the tribological behaviours are enhanced and the cutting efficiency maximized. There are three key turning factors such as, cutting speed, feed rate and depth of cut which were systematically varied to establish the corresponding machining characteristics and sustainability aspect such as carbon emission, energy consumed, cutting force and surface topography. The findings of mean GRG and ANOVA revealed that the parameter that would exert the most impact in achieving sustainable machining is the depth of cut. The optimal set of parameter values that was identified using GRA was the combination of the following parameters, cutting speed of 200 m/min and feed rate of 0.05 mm/rev and depth of 0.2 5 mm. The employment of such parameters, along with the application of the nano-enhanced dry turning conditions led to a 72.84 percent reduction in carbon, 72.82 percent energy, 18.58 percent of cutting power, and 6.83 percent of the surface roughness. There was also an increase in performance by 0.1702 in total GRG. As was observed in this paper, nano-assisted machining strategies and parameter optimisation can play a very crucial role in facilitating sustainable manufacturing of the aluminium-based hybrid composites.
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Reference
Aslani, K., Chaidas, D., Kechagias, J. D., Kyratsis, P. and Salonitis, K., Quality performance evaluation of thin-walled PLA 3D printed parts using the Taguchi method and grey relational analysis, J. Manuf. Mater. Process., 4(2), 47(2020).
http://doi.org/10.3390/jmmp4020047
Bademlioglu, A. H., Canbolat, A. S. and Kaynakli, O., Multi-objective optimization of parameters affecting Organic Rankine Cycle performance characteristics with Taguchi-Grey Relational Analysis, Renewable Sustainable Energy Rev., 117, 109483(2020).
http://doi.org/10.1016/j.rser.2019.109483
Bag, R., Panda, A., Sahoo, A. K. and Kumar, R., A brief study on effects of nano cutting fluids in hard turning of AISI 4340 steel, Mater. Today: Proc., 26, 3094–3099(2020).
https://doi.org/10.1016/j.matpr.2020.02.640
Canbolat, A. S., Bademlioglu, A. H., Arslanoglu, N. and Kaynakli, O., Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods, J. Cleaner Prod., 229, 874-885(2019).
http://doi.org/10.1016/J.JCLEPRO.2019.05.020
Ajith, A. D. S, Pugazhenthi, R. Kumar, R. and Vijayananth, S., Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi -grey relational analysis, Defence. Technology, 15(4), 545-556 (2019).
https://doi.org/10.1016/j.dt.2019.01.001
Dhanapal, A. R., Thiruvengadam, M., Vairavanathan, J., Venkidasamy, B., Easwaran, M. and Ghorbanpour, M., Nanotechnology Approaches for the Remediation of Agricultural Polluted Soils, ACS Omega, 9(12), 13522–13533(2024).
https://doi.org/10.1021/acsomega.3c09776
El-Ramady, H., Abdalla, N., Sári, D., Ferroudj, A., Muthu, A., Prokisch, J., Fawzy, Z. F., Brevik, E. C. and Solberg, S. Ø., Nanofarming: Promising Solutions for the Future of the Global Agricultural Industry, Agron., 13(6), 1600(2023).
https://doi.org/10.3390/agronomy13061600
Jagadeesha, T., Kunar, S., Kibria, G. and Nikam, M., Nano-Additives Assisted MQL and Optimization in Micromachining Processes, Electro-Micromachining Microfab., 193–204(2024).
https://doi.org/10.1201/9781003397793-9
Jung, J. and Kwon, W., Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis, J. Mech. Sci. Technol., 24, 1083–1090(2010).
http://doi.org/10.1007/S12206-010-0305-8
Karmiris O, P., Karkalos, N. E., Kudelski, R. and Markopoulos, A. P., Experimental study on the effect of the cooling method on surface topography and workpiece integrity during trochoidal end milling of Incoloy 800, Tribol. Int., 176, 107899(2022).
https://doi.org/10.1016/j.triboint.2022.107899
Khatai, S., Kumar S, A., Kumar, R. and Panda, A., Performance assessment of the multi-nozzle injection MQL system for cleaner machining of hardened AISI D2 steel concerning power consumption, Can. Metall. Q., 64(3), 1684–1707(2024).
https://doi.org/10.1080/00084433.2024.2393535
Khodabakhshi, F., Haghshenas, M., Chen, J., Amirkhiz, B. S., Li, J. and Gerlich, A., Bonding mechanism and interface characterisation during dissimilar friction stir welding of an aluminium/polymer bi-material joint, Sci. Technol. Weld. Joining, 22(3), 182–190(2017).
http://doi.org/10.1080/13621718.2016.1211583
Kumar, S., Uthayakumar, M., Kumaran, S. T., Parameswaran, P., Mohandas, E., Kempulraj, G., Babu, B. and Natarajan, S., Parametric optimization of wire electrical discharge machining on aluminium based composites through grey relational analysis, J. Manuf. Processes, 20, 33–39(2015).
http://doi.org/10.1016/J.JMAPRO.2015.09.011
Mahesh, K., Philip, J., Joshi, S. and Kuriachen, B., Machinability of Inconel 718: A critical review on the impact of cutting Temp. Mater. Manuf. Processes, 36(7), 753–791(2021).
http://doi.org/10.1080/10426914.2020.1843671
Mia, M., Gupta, M., Lozano, J. A., Carou, D., Pimenov, D., Królczyk, G., Khan, A. and Dhar, N. R., Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V, J. Cleaner Prod., 210, 121-133(2019).
http://doi.org/10.1016/J.JCLEPRO.2018.10.334
Omiyale, B. O., Olugbade, T., Abioye, T. and Farayibi, P., Wire arc additive manufacturing of aluminium alloys for aerospace and automotive applications: A review, Mater. Sci. Technol., 38(7), 391–408(2022).
http://doi.org/10.1080/02670836.2022.2045549
Pradhan, M. K., Estimating the effect of process parameters on MRR, TWR and radial overcut of EDMed AISI D2 tool steel by RSM and GRA coupled with PCA. Int. J. Adv. Manuf. Technol., 68, 591–605(2013).
http://doi.org/10.1007/S00170-013-4780-9
Rahman, A. H., Jouini, N., Ghani, J. A. and Rasani, M. R. M. A., Review of High-Speed Turning of AISI 4340 Steel with Minimum Quantity Lubrication (MQL), Coat., 14(8), 1063(2024).
https://doi.org/10.3390/coatings14081063
Rajeswari, B. and Amirthagadeswaran, K., Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis, Meas., 105, 78–86(2017).
http://doi.org/10.1016/J.MEASUREMENT.2017.04.014
Selvaraj, D., Chandramohan, P. and Mohanraj, M., Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method, Meas,, 49, 205–215(2014).
http://doi.org/10.1016/J.MEASUREMENT.2013.11.037
Shafey, A. M. E., Green synthesis of metal and metal oxide nanoparticles from plant leaf extracts and their applications: A review, Green Process. Synth., 9(1), 304–339(2020).
https://doi.org/10.1515/gps-2020-0031
Shaikh, S., Shaikh, V. A., Rao, V. M. and Patil, B. T., A105 Work-Material Turning Experimentation using L9 Orthogonal Array Runs with Dry, MQL and Nano-Al2O3 Assisted MQL Machining Conditions, OP Conf. Ser.: Mater. Sci. Eng., 1070(1), 012131(2021).
https://doi.org/10.1088/1757-899x/1070/1/012131
Younas, M., Jaffery, S., Khan, M., Khan, M. A., Ahmad, R., Mubashar, A. and Ali, L., Multi-objective optimization for sustainable turning Ti6Al4V alloy using grey relational analysis (GRA) based on analytic hierarchy process (AHP), Int. J. Adv. Manuf. Technol., 105, 1175–1188(2019).
