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ARTIFICIAL NEURAL NETWORK MODELING OF TRIBOLOGICALPARAMETERS OPTIMIZATION OF AZ31-SiC METAL MATRIX COMPOSITE
Authors:
Kothuri Chenchu Kishor Kumar1
,
Bandlamudi Raghu Kumar2
,
Nalluri Mohan Rao3
1Gudlavalleru Engineering College, Gudlavalleru-521 356, India
2Prasad V Potluri Siddhartha Institute of Technology, Vijayawada-520001, India
3Jawaharlal Nehru Technological University Kakinada, Kakinada-533003, India
Received: 5 January 2023
Revised: 8 June 2023
Accepted: 2 July 2023
Published: 30 September 2023
Abstract:
This paper focuses on modeling the tribological properties of AZ31-SiC composite using an artificial neural network (ANN) fabricated through the stir casting method. The twenty-seven tests were performed with three loads (10 N, 15 N, and 20 N), three sliding speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and three sliding distances (500 m, 750 m, and 1000 m) on wear testing machine and are used in the formation of training sets of ANN. Using the wear test data, Taguchi, Analysis of Variance (ANOVA), and regression analysis were carried out to determine the effect of the control parameters on the wear and coefficient of friction (COF). The experimental results demonstrate that the wear rate increases with an increase in load and distance and decreases with an increase in velocity. In addition, an alternative method is proposed to predict the wear and COF using ANN modeling with single and multi-hidden layer techniques. With good training, ANN gives accurate and close results to the experimental results. The results obtained using ANN modeling have a percentage of error of 4.71% and 5.79% for wear and COF respectively, when compared to experimental values. This prediction process saves time and costs for the manufacturer.
Keywords:
AZ31 alloy, silicon carbide, wear parameters, optimization, artificial neural network, modeling
References:
[1] Y. Huang, Q. Ouyang, D. Zhang, J. Zhu, R. Li, H. Yu, Carbon Materials Reinforced Aluminum Composites: A Review. Acta Metallurgica Sinica, 27, 2014: 775-786. https://doi.org/10.1007/s40195-014-0160-1
[2] A. Vencl, N.M. Vaxevanidis, M. Kandeva, A bibliometric analysis of scientific research on tribology of composites in Southeastern Europe. IOP Conference Series: Materials Science and Engineering, 724, 2020: 012012. https://doi.org/10.1088/1757-899X/724/1/012012
[3] P.K. Kumar , N.V. Sai, A.G. Krishna, Effect of Y2O3 addition and cooling rate on mechanical properties of Fe-24Cr-20Ni-2Mn steels by powder metallurgy route. Composite Communication, 10, 2018: 116-121. https://doi.org/10.1016/j.coco.2018.09.003
[4] V. Sklenicka, M. Svoboda, M. Pahutova, K, Kucharova T.G. Langdon, Microstructural processes in creep of an AZ 91 magnesium – based composite and its matrix alloy. Material Science and Engineering A, 319-321, 2001: 741-745. https://doi.org/10.1016/S0921-5093(01)01023-1
[5] X.-l. Zhang, G.-k. Yu, W.-b. Zou, Y.-s. J, Y.-z. Liu, J.-l. Cheng, Effect of casting methods on microstructure and mechanical properties of ZM5 space flight magnesium alloy. China Foundry, 15, 2018: 418-421.
https://doi.org/10.1007/s41230-018-8098-y
[6] S.-J. Huang, Y.-R. Jeng, V.I. Semenov, Y.-Z. Dai, Particle size effects of silicon carbide on wear behavior of SiC p-reinforced magnesium matrix composites. Tribology Letters, 42, 2011: 79-87.
https://doi.org/10.1007/s11249-011-9751-4
[7] S.F. Hassan, N.O. Ogunlakin, N. Al-Aqeeli, S. Nouari, M.M.A. Baig, F. Patel, Development of tensile-compressive asymmetry free magnesium based composite using TiO2 nanoparticles dispersion. Journal of Materials Research, 33, 2018: 130-137. https://doi.org/10.1557/jmr.2017.430
[8] M. Rashad, F. Pan, H. Hu, M. Asif, S. Hussain, J. She, Enhanced tensile properties of magnesium composites reinforced with grapheme nanoplatelets. Materials Science Engineering A, 630, 2015: 36-44. https://doi.org/10.1016/j.msea.2015.02.002
[9] S. Basavarajappa, G. Chandramohan, K. Mukund, M. Ashwin, M. Prabu, Dry Sliding Wear Behavior of Al 2219/SiCp-Gr Hybrid Metal Matrix Composites. Journal of Materials Engineering and Performance, 15, 2006: 668-674. https://doi.org/10.1361/105994906X150803
[10] Q.B. Nguyen, Y.H.M. Sim, M. Gupta, C.Y.H. Lim, Tribology characteristics of magnesium alloy AZ31B and its composites. Tribology International, 82(Part B), 2015: 464-471.
https://doi.org/10.1016/j.triboint.2014.02.024
[11] S.-J. Huang, A. Negash Ali, Experimental investigations of effects of SiC contents and severe plastic deformation on the microstructure and mechanical properties of SiCp / AZ61 magnesium metal matrix composites. Journal of Materials Processing Technology, 272, 2019: 28-39.
https://doi.org/10.1016/j.jmatprotec.2019.05.002
[12] K.K. Deng, K. Wu, Y.W. Wu, K.B. Nie, M.Y. Zheng, Effect of submicron size SiC particulates on microstructure and mechanical properties of AZ91 magnesium matrix composites. Journal of alloys and compounds, 504(2), 2010: 542-547. https://doi.org/10.1016/j.jallcom.2010.05.159
[13] K.B. Nie, X.J. Wang, K. Wu, X.S. Hu, M.Y. Zheng, L. Xu, Microstructure and tensile properties of micro-SiC particles reinforced magnesium matrix composites produced by semisolid stirring assisted ultrasonic vibration. Materials Science and Engineering A, 528(29-30), 2011: 8709-8714.
https://doi.org/10.1016/j.msea.2011.08.035
[14] A. Asgari, M. Sedighi, P. Krajnik, Magnesium alloy-silicon carbide composite fabrication using chips Waste. Journal of Cleaner Production, 232, 2019: 1187-1194. https://doi.org/10.1016/j.jclepro.2019.06.018
[15] K.B. Nie, X.J. Wang, K. Wu, L. Xu, M.Y. Zheng, X.S. Hu, Fabrication of SiC particles-reinforced magnesium matrix composite by ultrasonic vibration. Journal of Materials Science, 47, 2012: 138-144.
https://doi.org/10.1007/s10853-011-5780-5
[16] S. Sathish, V. Anandakrishnan, S. Sankaranarayanan, M. Gupta, Optimization of wear parameters of magnesium metal-metal composite using Taguchi and GA technique. Journal Tribologi, 23, 2019: 76-89. https://doi.org/10.1590/1980-5373-MR-2022-0467
[17] S.K. Khatkar, R. Verma, Sumankant, S.S. Kharb, A. Thakur, R. Sharma, Optimization and Effect of Reinforcements on the Sliding Wear Behavior of Self-Lubricating AZ91D-SiC- Gr Hybrid Composites. Silicon, 13, 2021: 1461- 1473. https://doi.org/10.1007/s12633-020-00523-0
[18] C. Sankar, K. Gangatharan, S.C.E. Singh, R.K. Sharma, K. Mayandi, Optimization on Tribological Behaviour of Milled Nano-B 4 C Particles Reinforced with AZ91 Alloy Through Powder Metallurgy Method. Transactions of Indian Institute of Metals, 72, 2019: 1255- 1275. https://doi.org/10.1007/s12666-019-01618-y
[19] B.M. Girish, B.M. Satish, S. Sarapure, Basawaraj, Optimization of Wear Behavior of Magnesium Alloy AZ91 Hybrid Composites Using Taguchi Experimental Design. Metallurgical and Materials Transactions A, 47, 2016: 3193-3200. https://doi.org/10.1007/s11661-016-3447-1
[20] S. Ghalme, A. Mankar, Y. Bhalerao, Integrated Taguchi-simulated annealing (SA) approach for analyzing wear behaviour of silicon nitride. Journal of Applied Research and Technology, 15(6), 2017: 624-632.
https://doi.org/10.1016/j.jart.2017.08.003
[21] M.-C. Chen, D.-M. Tsai, A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production and Research, 34(10), 1996, 2803- 2825.
https://doi.org/10.1080/00207549608905060
[22] S. Mirjalili, The Ant Lion Optimizer. Advances in Engineering Softwares, 83, 2015: 80-98.
https://doi.org/10.1016/j.advengsoft.2015.01.010
[23] A.G. Joshi, M. Manjaiah, S. Basavarajappa, R. Sures, Wear Performance Optimization of SiC-Gr Reinforced Al Hybrid Metal Matrix Composites Using Integrated Regression- Antlion Algorithm. Silicon, 13, 2021: 3941- 3951. https://doi.org/10.1007/s12633-020-00704-x
[24] K.C.K Kumar, B.R. Kumar, N.M. Rao, Tribological Parameters Optimization of AZ31-SiC Composite Using Whale Optimization Algorithm. Journal of Materials Engineering and Performance, 32, 2023: 2735-2748. https://doi.org/10.1007/s11665-022-07570-1
[25] K.C.K Kumar, B.R. Kumar, N.M. Rao, Microstructural, Mechanical Characterization, and Fractography of AZ31/SiC Reinforced Composites by Stir Casting Method. Silicon, 14 2022: 5017-5027.
https://doi.org/10.1007/s12633-021-01180-7
[26] J.-Lian Wen, J.-R. Shie, Y.-K. Yang, Optimization of a Wear Property of Die Cast AZ91D Components via a Neural Network. Materials and Manufacturing Processes, 24(4), 2009: 400-408.
https://doi.org/10.1080/10426910802714274
[27] D. Nayak, N. Ray, R. Sahoo, M. Debata, Analysis of Tribological Performance of Cu Hybrid Composites Reinforced with Graphite and TiC Using Factorial Techniques. Tribology Transactions, 57(5), 2014: 908-918. https://doi.org/10.1080/10402004.2014.923079
[28] M.O. Bodunrin, K.K. Alaneme, L.H. Chown, Aluminium matrix hybrid composites: A review of reinforcement philosophies; mechanical, corrosion and tribological characteristics. Journal of Materials Research and Technology, 4(4), 2015: 434-445. https://doi.org/10.1016/j.jmrt.2015.05.003
[29] N. Radhika, R. Subramaniam, Wear behaviour of aluminium/alumina/graphite hybrid metal matrix composites using Taguchi’s techniques. Industrial Lubrication and Tribology, 65(3), 2013, 166-174. https://doi.org/10.1108/00368791311311169
[30] A.H.S. Rahiman, D.S.R. Smart, B. Wilson, I. Ebrahim, B. Eldhose, B. Mathew, R.T. Murickan, Dry sliding wear analysis OF Al5083/CNT/Ni/MoB hybrid composite using DOE Taguchi method. Wear, 460-461, 2020: 203471. https://doi.org/10.1016/j.wear.2020.203471
[31] S. Veličković, B. Stojanović, M. Babić, I. Bobić, Optimization of tribological properties of aluminum hybrid composites using Taguchi design. Journal of Composite Materials, 51(17), 2017: 1-11.
https://doi.org/10.1177/0021998316672294
[32] S.D. Saravanan, M. Senthilkumar, Prediction of tribological behaviour of rice husk ash reinforced aluminum alloy matrix composites using artificial neural network. Russian Journal of Non-Ferreous Metals, 56 2015: 97-106. https://doi.org/10.3103/S1067821215010174
[33] A. Fathy, AA. Megahed, Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks. The International Journal of Advances Manufacturing Technology, 62, 2012: 953- 963. https://doi.org/10.1007/s00170-011-3861-x
[34] Z.Y. Jiang, Z. Zhang, K. Friedrich, Prediction on wear properties of polymer composites with artificial neural networks. Composites Science and Technology, 67(2), 2007: 168-176.
https://doi.org/10.1016/j.compscitech.2006.07.026
[35] R. Egala, G.V. Jagadeesh, S.G. Setti, Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites. Friction, 9, 2019, 250-272.
https://doi.org/10.1007/s40544-019-0332-0
[36] F. Alambeigi, S.M. Khadem, H. Khorsand, E.M.S. Hasan, A comparison of performance of artificial intelligence methods in prediction of dry sliding wear behaviour. The International Journal of Advances Manufacturing Technology, 84, 2016: 1981- 1994. https://doi.org/10.1007/s00170-015-7812-9
© 2023 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 1
March 2025
Last Edition
Volume 10
Number 1
March 2025
How to Cite
K.C.K. Kumar, B.R. Kumar, N.M. Rao, Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
More Citation Formats
Kumar, K.C.K., Kumar, B.R., Rao, N.M. (2023). Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
Kumar, Kothuri Chenchu Kishor, et al. “Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite.“ Applied Engineering Letters, vol. 8, no. 3, 2023, pp. 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
Kumar, Kothuri Chenchu Kishor, Bandlamudi Raghu Kumar, and Nalluri Mohan Rao. 2023. “Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite.” Applied Engineering Letters, 8 (3): 111–20. https://doi.org/10.18485/aeletters.2023.8.3.3.
Kumar, K.C.K., Kumar, B.R. and Rao, N.M. (2023). Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: pp. 111–120.
doi: 10.18485/aeletters.2023.8.3.3.
ARTIFICIAL NEURAL NETWORK MODELING OF TRIBOLOGICALPARAMETERS OPTIMIZATION OF AZ31-SiC METAL MATRIX COMPOSITE
Authors:
Kothuri Chenchu Kishor Kumar1
,
Bandlamudi Raghu Kumar2
,
Nalluri Mohan Rao3
1Gudlavalleru Engineering College, Gudlavalleru-521 356, India
2Prasad V Potluri Siddhartha Institute of Technology, Vijayawada-520001, India
3Jawaharlal Nehru Technological University Kakinada, Kakinada-533003, India
Received: 5 January 2023
Revised: 8 June 2023
Accepted: 2 July 2023
Published: 30 September 2023
Abstract:
This paper focuses on modeling the tribological properties of AZ31-SiC composite using an artificial neural network (ANN) fabricated through the stir casting method. The twenty-seven tests were performed with three loads (10 N, 15 N, and 20 N), three sliding speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and three sliding distances (500 m, 750 m, and 1000 m) on wear testing machine and are used in the formation of training sets of ANN. Using the wear test data, Taguchi, Analysis of Variance (ANOVA), and regression analysis were carried out to determine the effect of the control parameters on the wear and coefficient of friction (COF). The experimental results demonstrate that the wear rate increases with an increase in load and distance and decreases with an increase in velocity. In addition, an alternative method is proposed to predict the wear and COF using ANN modeling with single and multi-hidden layer techniques. With good training, ANN gives accurate and close results to the experimental results. The results obtained using ANN modeling have a percentage of error of 4.71% and 5.79% for wear and COF respectively, when compared to experimental values. This prediction process saves time and costs for the manufacturer.
Keywords:
AZ31 alloy, silicon carbide, wear parameters, optimization, artificial neural network, modeling
References:
[1] Y. Huang, Q. Ouyang, D. Zhang, J. Zhu, R. Li, H. Yu, Carbon Materials Reinforced Aluminum Composites: A Review. Acta Metallurgica Sinica, 27, 2014: 775-786. https://doi.org/10.1007/s40195-014-0160-1
[2] A. Vencl, N.M. Vaxevanidis, M. Kandeva, A bibliometric analysis of scientific research on tribology of composites in Southeastern Europe. IOP Conference Series: Materials Science and Engineering, 724, 2020: 012012. https://doi.org/10.1088/1757-899X/724/1/012012
[3] P.K. Kumar , N.V. Sai, A.G. Krishna, Effect of Y2O3 addition and cooling rate on mechanical properties of Fe-24Cr-20Ni-2Mn steels by powder metallurgy route. Composite Communication, 10, 2018: 116-121. https://doi.org/10.1016/j.coco.2018.09.003
[4] V. Sklenicka, M. Svoboda, M. Pahutova, K, Kucharova T.G. Langdon, Microstructural processes in creep of an AZ 91 magnesium – based composite and its matrix alloy. Material Science and Engineering A, 319-321, 2001: 741-745. https://doi.org/10.1016/S0921-5093(01)01023-1
[5] X.-l. Zhang, G.-k. Yu, W.-b. Zou, Y.-s. J, Y.-z. Liu, J.-l. Cheng, Effect of casting methods on microstructure and mechanical properties of ZM5 space flight magnesium alloy. China Foundry, 15, 2018: 418-421.
https://doi.org/10.1007/s41230-018-8098-y
[6] S.-J. Huang, Y.-R. Jeng, V.I. Semenov, Y.-Z. Dai, Particle size effects of silicon carbide on wear behavior of SiC p-reinforced magnesium matrix composites. Tribology Letters, 42, 2011: 79-87.
https://doi.org/10.1007/s11249-011-9751-4
[7] S.F. Hassan, N.O. Ogunlakin, N. Al-Aqeeli, S. Nouari, M.M.A. Baig, F. Patel, Development of tensile-compressive asymmetry free magnesium based composite using TiO2 nanoparticles dispersion. Journal of Materials Research, 33, 2018: 130-137. https://doi.org/10.1557/jmr.2017.430
[8] M. Rashad, F. Pan, H. Hu, M. Asif, S. Hussain, J. She, Enhanced tensile properties of magnesium composites reinforced with grapheme nanoplatelets. Materials Science Engineering A, 630, 2015: 36-44. https://doi.org/10.1016/j.msea.2015.02.002
[9] S. Basavarajappa, G. Chandramohan, K. Mukund, M. Ashwin, M. Prabu, Dry Sliding Wear Behavior of Al 2219/SiCp-Gr Hybrid Metal Matrix Composites. Journal of Materials Engineering and Performance, 15, 2006: 668-674. https://doi.org/10.1361/105994906X150803
[10] Q.B. Nguyen, Y.H.M. Sim, M. Gupta, C.Y.H. Lim, Tribology characteristics of magnesium alloy AZ31B and its composites. Tribology International, 82(Part B), 2015: 464-471.
https://doi.org/10.1016/j.triboint.2014.02.024
[11] S.-J. Huang, A. Negash Ali, Experimental investigations of effects of SiC contents and severe plastic deformation on the microstructure and mechanical properties of SiCp / AZ61 magnesium metal matrix composites. Journal of Materials Processing Technology, 272, 2019: 28-39.
https://doi.org/10.1016/j.jmatprotec.2019.05.002
[12] K.K. Deng, K. Wu, Y.W. Wu, K.B. Nie, M.Y. Zheng, Effect of submicron size SiC particulates on microstructure and mechanical properties of AZ91 magnesium matrix composites. Journal of alloys and compounds, 504(2), 2010: 542-547. https://doi.org/10.1016/j.jallcom.2010.05.159
[13] K.B. Nie, X.J. Wang, K. Wu, X.S. Hu, M.Y. Zheng, L. Xu, Microstructure and tensile properties of micro-SiC particles reinforced magnesium matrix composites produced by semisolid stirring assisted ultrasonic vibration. Materials Science and Engineering A, 528(29-30), 2011: 8709-8714.
https://doi.org/10.1016/j.msea.2011.08.035
[14] A. Asgari, M. Sedighi, P. Krajnik, Magnesium alloy-silicon carbide composite fabrication using chips Waste. Journal of Cleaner Production, 232, 2019: 1187-1194. https://doi.org/10.1016/j.jclepro.2019.06.018
[15] K.B. Nie, X.J. Wang, K. Wu, L. Xu, M.Y. Zheng, X.S. Hu, Fabrication of SiC particles-reinforced magnesium matrix composite by ultrasonic vibration. Journal of Materials Science, 47, 2012: 138-144.
https://doi.org/10.1007/s10853-011-5780-5
[16] S. Sathish, V. Anandakrishnan, S. Sankaranarayanan, M. Gupta, Optimization of wear parameters of magnesium metal-metal composite using Taguchi and GA technique. Journal Tribologi, 23, 2019: 76-89. https://doi.org/10.1590/1980-5373-MR-2022-0467
[17] S.K. Khatkar, R. Verma, Sumankant, S.S. Kharb, A. Thakur, R. Sharma, Optimization and Effect of Reinforcements on the Sliding Wear Behavior of Self-Lubricating AZ91D-SiC- Gr Hybrid Composites. Silicon, 13, 2021: 1461- 1473. https://doi.org/10.1007/s12633-020-00523-0
[18] C. Sankar, K. Gangatharan, S.C.E. Singh, R.K. Sharma, K. Mayandi, Optimization on Tribological Behaviour of Milled Nano-B 4 C Particles Reinforced with AZ91 Alloy Through Powder Metallurgy Method. Transactions of Indian Institute of Metals, 72, 2019: 1255- 1275. https://doi.org/10.1007/s12666-019-01618-y
[19] B.M. Girish, B.M. Satish, S. Sarapure, Basawaraj, Optimization of Wear Behavior of Magnesium Alloy AZ91 Hybrid Composites Using Taguchi Experimental Design. Metallurgical and Materials Transactions A, 47, 2016: 3193-3200. https://doi.org/10.1007/s11661-016-3447-1
[20] S. Ghalme, A. Mankar, Y. Bhalerao, Integrated Taguchi-simulated annealing (SA) approach for analyzing wear behaviour of silicon nitride. Journal of Applied Research and Technology, 15(6), 2017: 624-632.
https://doi.org/10.1016/j.jart.2017.08.003
[21] M.-C. Chen, D.-M. Tsai, A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production and Research, 34(10), 1996, 2803- 2825.
https://doi.org/10.1080/00207549608905060
[22] S. Mirjalili, The Ant Lion Optimizer. Advances in Engineering Softwares, 83, 2015: 80-98.
https://doi.org/10.1016/j.advengsoft.2015.01.010
[23] A.G. Joshi, M. Manjaiah, S. Basavarajappa, R. Sures, Wear Performance Optimization of SiC-Gr Reinforced Al Hybrid Metal Matrix Composites Using Integrated Regression- Antlion Algorithm. Silicon, 13, 2021: 3941- 3951. https://doi.org/10.1007/s12633-020-00704-x
[24] K.C.K Kumar, B.R. Kumar, N.M. Rao, Tribological Parameters Optimization of AZ31-SiC Composite Using Whale Optimization Algorithm. Journal of Materials Engineering and Performance, 32, 2023: 2735-2748. https://doi.org/10.1007/s11665-022-07570-1
[25] K.C.K Kumar, B.R. Kumar, N.M. Rao, Microstructural, Mechanical Characterization, and Fractography of AZ31/SiC Reinforced Composites by Stir Casting Method. Silicon, 14 2022: 5017-5027.
https://doi.org/10.1007/s12633-021-01180-7
[26] J.-Lian Wen, J.-R. Shie, Y.-K. Yang, Optimization of a Wear Property of Die Cast AZ91D Components via a Neural Network. Materials and Manufacturing Processes, 24(4), 2009: 400-408.
https://doi.org/10.1080/10426910802714274
[27] D. Nayak, N. Ray, R. Sahoo, M. Debata, Analysis of Tribological Performance of Cu Hybrid Composites Reinforced with Graphite and TiC Using Factorial Techniques. Tribology Transactions, 57(5), 2014: 908-918. https://doi.org/10.1080/10402004.2014.923079
[28] M.O. Bodunrin, K.K. Alaneme, L.H. Chown, Aluminium matrix hybrid composites: A review of reinforcement philosophies; mechanical, corrosion and tribological characteristics. Journal of Materials Research and Technology, 4(4), 2015: 434-445. https://doi.org/10.1016/j.jmrt.2015.05.003
[29] N. Radhika, R. Subramaniam, Wear behaviour of aluminium/alumina/graphite hybrid metal matrix composites using Taguchi’s techniques. Industrial Lubrication and Tribology, 65(3), 2013, 166-174. https://doi.org/10.1108/00368791311311169
[30] A.H.S. Rahiman, D.S.R. Smart, B. Wilson, I. Ebrahim, B. Eldhose, B. Mathew, R.T. Murickan, Dry sliding wear analysis OF Al5083/CNT/Ni/MoB hybrid composite using DOE Taguchi method. Wear, 460-461, 2020: 203471. https://doi.org/10.1016/j.wear.2020.203471
[31] S. Veličković, B. Stojanović, M. Babić, I. Bobić, Optimization of tribological properties of aluminum hybrid composites using Taguchi design. Journal of Composite Materials, 51(17), 2017: 1-11.
https://doi.org/10.1177/0021998316672294
[32] S.D. Saravanan, M. Senthilkumar, Prediction of tribological behaviour of rice husk ash reinforced aluminum alloy matrix composites using artificial neural network. Russian Journal of Non-Ferreous Metals, 56 2015: 97-106. https://doi.org/10.3103/S1067821215010174
[33] A. Fathy, AA. Megahed, Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks. The International Journal of Advances Manufacturing Technology, 62, 2012: 953- 963. https://doi.org/10.1007/s00170-011-3861-x
[34] Z.Y. Jiang, Z. Zhang, K. Friedrich, Prediction on wear properties of polymer composites with artificial neural networks. Composites Science and Technology, 67(2), 2007: 168-176.
https://doi.org/10.1016/j.compscitech.2006.07.026
[35] R. Egala, G.V. Jagadeesh, S.G. Setti, Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites. Friction, 9, 2019, 250-272.
https://doi.org/10.1007/s40544-019-0332-0
[36] F. Alambeigi, S.M. Khadem, H. Khorsand, E.M.S. Hasan, A comparison of performance of artificial intelligence methods in prediction of dry sliding wear behaviour. The International Journal of Advances Manufacturing Technology, 84, 2016: 1981- 1994. https://doi.org/10.1007/s00170-015-7812-9
© 2023 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 1
March 2025
Last Edition
Volume 10
Number 1
March 2025
How to Cite
K.C.K. Kumar, B.R. Kumar, N.M. Rao, Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
More Citation Formats
Kumar, K.C.K., Kumar, B.R., Rao, N.M. (2023). Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
Kumar, Kothuri Chenchu Kishor, et al. “Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite.“ Applied Engineering Letters, vol. 8, no. 3, 2023, pp. 111–120.
https://doi.org/10.18485/aeletters.2023.8.3.3
Kumar, Kothuri Chenchu Kishor, Bandlamudi Raghu Kumar, and Nalluri Mohan Rao. 2023. “Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite.” Applied Engineering Letters, 8 (3): 111–20. https://doi.org/10.18485/aeletters.2023.8.3.3.
Kumar, K.C.K., Kumar, B.R. and Rao, N.M. (2023). Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite. Applied Engineering Letters, 8(3), 2023: pp. 111–120.
doi: 10.18485/aeletters.2023.8.3.3.