Wear Performance Evaluation of Nano Zirconium Diboride Developed ZK60 Nanocomposite
J. Environ. Nanotechnol., Volume 13, No 4 (2024) pp. 281-286
Abstract
This study intended to apply an artificial neural network (ANN) technique to forecast the wear rate (WR) of a ZK60- 8% ZrB2 nanocomposite. The load, sliding velocity, and sliding distance on the pin and disc were all factors included during the development of the predictive model. The wear rate from the L16 full factorial tests was used as an input in the model. According to the data, the forecast for all wear rates demonstrated a level of accuracy. Thus, ANN enables the prediction of wear performance of the composite material. Applying the ANN model to the data results in effectiveness and precision. In addition, it has the potential to assist researchers in the development and execution of their discoveries, reducing the amount of time required for lengthy experimental initiatives.
Full Text
Reference
Aruna, M., Mohana, K. A., Prabagaran, S., Priya, C. B. and Abul Kalam, M., Semisolid stir processing of AZ91/TiO2 nanocomposite analysis with SiC exposure: behaviour measures, Int. J. Cast Met. Res., 37(3), 159-166 (2024).
https://doi.org/10.1080/13640461.2024.2327016
Aydın, F., Tribological aspects of magnesium matrix composites: a review of recent experimental studies. Tribology - Materials, Surf. Interfaces.,17(4), 1-10 (2023).
https://doi.org/10.1080/17515831.2023.2246809
Bommala, V. K., Krishna, M. G. and Rao, C. T., Magnesium matrix composites for biomedical applications: A review, J Magnes Alloys.,7(1), 72-79 (2019).
https://doi.org/10.1016/j.jma.2018.11.001
Boztas, H., Esen, I., Ahlatci, H. and Turen, Y., Microstructure Characterization and Wear Behavior of New ZK60 Alloy Reinforced with 5–10% SiC and 5–10% B4C Particles, J. Mater. Eng. Perform., 33, 7413-7427 (2024).
https://doi.org/10.1007/s11665-023-08469-1
Chukwuma, A., Godwills, C., Nwaeju, C. and Onyemachi, O., Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium, Int. J. Nonferrous Metall., 11, 1-19 (2024).
https://doi.org/10.4236/ijnm.2024.111001
Egbo, M. K., A fundamental review on composite materials and some of their applications in biomedical engineering, J. King Saud Univ. Eng. Sci.,33(8), 557-568 (2021).
https://doi.org/10.1016/j.jksues.2020.07.007
Hossain, I., Aruna, M., Mohana Krishnan, A., Prabagaran, S., Venkatesh, R., Priya, C. B. and Abul, K. M., Silicon carbide nanoparticles featured AZ63/BN alloy nanocomposite made by using liquid stir vacuum die cast: characteristics study, Int. J. Cast Met. Res., 37(2), 148-157 (2024).
https://doi.org/10.1080/13640461.2024.2327015
Jayasankar, K. C., Anandhakumar, G. and Kalaimurugan, A., Advancements and Challenges in Solar Radiation Prediction: A Review of Machine Learning Approaches, Environ. Technol., 13(2), 60-64 (2024).
https://doi.org/10.13074/jent.2024.06.242623
Karthik, R., Shiva, S. N. and Venkatesh, R. K., Characteristics performance evaluation of AZ91-fly ash composite developed by vacuum associated stir processing, Int. J. Cast Met. Res., 2, 1–8 (2024).
https://doi.org/10.1080/13640461.2024.2364129
Khatkar, S. K., Hybrid magnesium matrix composites: A review of reinforcement philosophies, mechanical and tribological characteristics, Rev. Adv. Mater. Sci., 2022, 0294 (2023)
https://doi.org/10.1515/rams-2022-0294
Kumar, P. M., Saravanakumar, R., Karthick, A. and Mohanavel, V., Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system, Environ. Sci. Pollut. Res., 29(7), 10173–10182 (2022).
https://doi.org/10.1007/s11356-021-16398-6
Kumar., Suresh, S. and Mohanavel, V., Investigations on physical, mechanical and metallurgical characteristics of ZK60/ZrB2 composites produced by stir casting route, Matéria (Rio de Janeiro), 29(3), 15-20 (2024).
https://doi.org/10.1590/1517-7076-RMAT-2024-0264
Kumar, S. S. and V. Mohanavel., Optimization of Tribological parameter of ZK60/12 Wt% ZrB2 composite through Taguchi approach, Interactions., 245, 208 (2024).
https://doi.org/10.1007/s10751-024-02054-1
Kumar, D., Phanden, R. K. and Thakur, L., A review on environment friendly and lightweight Magnesium-Based metal matrix composites and alloys, Mater., 38, 359-364 (2020).
https://doi.org/10.1016/j.matpr.2020.07.424
Liu, X., Tian, S., Tao, F. and Yu, W., A review of artificial neural networks in the constitutive modeling of composite materials, Compos. B. Eng., 224, 109152 (2021).
https://doi.org/10.1016/j.compositesb.2021.109152
Mousavi, S. F., Sharifi, H., Tayebi, M., Hamawandi, B. and Behnamian, Y., Thermal cycles behaviour and microstructure of AZ31/SiC composite prepared by stir casting, Sci. Rep., 12(1), 15191 (2022).
https://doi.org/10.1038/s41598-022-19410-2
Padmavathi, K. R., Devi, G. R. and Muthukumar, V., Synthesis and Characteristics Evaluation of SiCnp and SiCnp/CNT-Reinforced AZ91D Alloy Hybrid Nanocomposites Via Semisolid Stir Casting Technique, Int. J. Met., 18(2), 1633–1643 (2024).
https://doi.org/10.1007/s40962-023-01137-z
Periasamy, K., Ganesh, S., Chandra Kumar, S., Nandhakumar, S. and Gurmesa, M. D., Appraisal of Thermomechanical Performance of Aluminum Metal Matrix Composites Using Stir Casting Technique, Adv. Mater. Sci. Eng., 2022(1), 2381425 (2022).
https://doi.org/10.1155/2022/2381425
Roseline, J. F., Dhanya, D., Selvan, S., Yuvaraj, M., Duraipandy, P., Kumar, S. S. and Mohanavel, V., Neural Network modelling for prediction of energy in hybrid renewable energy systems, Energy Rep., 8, 999–1008 (2022).
https://doi.org/10.1016/j.egyr.2022.10.284
Saleh, B., Ma, A., Fathi, R., Radhika, N., Yang, G. and Jiang, J., Optimized mechanical properties of magnesium matrix composites using RSM and ANN, J. Mater. Sci. Eng., B., 290, 116303 (2023).
https://doi.org/10.1016/j.mseb.2023.116303
Sankhla, A. and Patel, K. M., Metal Matrix Composites Fabricated by Stir Casting Process–A Review,Adv. Mater. and Pro. Tech.,8(2), 1270-1291 (2022).
https://doi.org/10.1080/2374068X.2020.1855404
Senthilkumar, S., Revathi, K. and Sivaprakash, E., Enhancement of Magnesium Alloy (AZ31B) Nanocomposite by the Additions of Zirconia Nanoparticle Via Stir Casting Technique: Physical, Microstructural, and Mechanical Behaviour,Int. J. Met., 18(2), 1465–1474 (2024).
https://doi.org/10.1007/s40962-023-01116-4
Singh, R. and Hashmi, M. S. J., Metal matrix composite: a methodological review, Adv. Mater. and Pro. Tech., 6(1), 13-24 (2020).
https://doi.org/10.1080/2374068X.2019.1682296
Tayebi, M., Nategh, S., Najafi, H. and Khodabandeh, A., Tensile properties and microstructure of ZK60/SiCw composite after extrusion and ageing, J. Alloys Compd., 830, 154709 (2020).
https://doi.org/10.1016/j.jallcom.2020.154709
Thangavel, T. S., Jeyaseelan, C., Paramathma, B. S. and Mahadevan, K., Experimental Investigation of Silicon Carbide Nanoparticles Reinforced Magnesium Alloy (AZ91E) Metal Matrix Composite by Vacuum Stir Casting Method, SAE Tech. Pap., 28, 0169 (2019).
https://doi.org/10.4271/2019-28-0169
Thirugnanasambandham, T., Chandradass, J., Venkatesh, R., Sharma, P., Elahi, M. and Almoallim, H. S., Silicon carbide-embedded AZ91E alloy nanocomposite hybridized with chopped basalt fibre: performance measures, Int. J. Cast Met. Res., 2, 1-11 (2024)
https://doi.org/10.1080/13640461.2024.2395124
Venkatesh, R., Sakthivel, P., Selvakumar, G., Krishnan, A. M., Purushothaman, P. and Priya, C. B. Mechanical and thermal properties of a waste fly ash-bonded Al-10 Mg alloy composite improved by bioceramic silicon nanoparticles, Biomass Convers. Biorefin., 14, 1-12 (2023).
https://doi.org/10.1007/s13399-023-04588-w
Wang, Y., Soutis, C., Ando, D., Sutou, Y. and Narita, F., Application of deep neural network learning in composites design, EJMSE., 2(1), 117-170 (2022)
https://doi.org/10.1080/26889277.2022.2053302
Wąsik, A., Leszczyńska-Madej, B. and Madej, M., Sustainability in the Manufacturing of Eco-Friendly Aluminum Matrix Composite Materials, Sustainability., 16(2), 903 (2024).
https://doi.org/10.3390/su16020903
Wiciak-Pikuła, M., Felusiak-Czyryca, A. and Twardowski, P., Tool wear prediction based on artificial neural network during aluminium matrix composite milling, Sensors (Switzerland.), 20(20), 5798 (2020).
https://doi.org/10.3390/s20205798
Zhao, L., Zheng, W., Hu, Y., Guo, Q. and Zhang, D., Heterostructured metal matrix composites for structural applications: a review, J. Mater. Sci., 59, 9768-9801 (2024).
