Abstract
Aluminium Alloys AA5083 dispersed with varying fractions of reinforcement was fabricated through the stir casting method. In varying weight percentage combinations, zinc oxide (ZnO) and coconut shell ash (CSA) particles were combined to create hybrid reinforcement particles. Using a pin-on-disc tribometer, the wear characteristics of the developed AA5083 hybrid composites were estimated. The volumetric proportion of hybrid reinforcement particles CSA (3, 6, 9 and 3 ZnO wt%), load (20, 30, 40 N), sliding velocity (2, 3, and 4 m/s), Cumulative Time (4.16, 5.55, and 8.33 min), and sliding distance are some of the experimental parameters (1000 m). Wear analysis revealed effective bonding and homogeneous dispersion of hybrid reinforcement particles onto the AA5083. Analysis of Specific Wear Rate (SWR) results showed that Specific Wear Rate rose with load, sliding velocity, and sliding duration while decreasing with hybrid particle dispersion. This research proposes the use of several intelligent classification techniques using Machine Learning (ML) and Artificial Neural Network (ANN) to predict the wear rate of an AA 5083 hybrid composite. For estimating wear quantities, the algorithms Random Forest (RF), Neural Network (NN), and k-nearest neighbours (kNN) are utilized. Six inputs are utilized to train and evaluate the Machine Learning (ML) algorithms: the Applied Load (N), Sliding Velocity, Sliding Speed, Cumulative Time, Percentage of Reinforcements, and Sliding Distance. The output is the Specific Wear Rate (SWR). The RF, NN, and KNN algorithms all produced success rates of correlation between experimental to anticipated of 0.90, 0.84, and 0.90, respectively. The same model data was utilised to train and evaluate Artificial Neural Networks (ANN), with the Multilayer Perceptron (MLP) network having the lowest Mean Square Error (MSE) to improve machine learning prediction accuracy. Maximum estimate error range of 0.1%, training and cross-validation of 0.00000496 and 0.0261, respectively, with linear correlation coefficient in testing of 0.9999 or 99.9% better prediction accuracy rate. The AA 5083 composites were designed and implemented using this machine learning and artificial neural network model for forecasting specific wear rate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ikumapayi OM, Akinlabi ET, Pal SK, Majumdar JD (2019) A survey on reinforcements used in friction stir processing of aluminium metal matrix and hybrid composites. Proc Manuf 35:935–940
Sudherson DPS, Sunil J (2020) Dry sliding wear behaviour of novel AA5083-cadmium alloy prepared by stir casting process. Mater Today: Proc 21:142–147
Zhang T, Li DY (2001) Improvement in the resistance of aluminum with yttria particles to sliding wear in air and in a corrosive medium. Wear 251(1–12):1250–1256
Arulraj M, Palani PK (2018) Parametric optimization for improving impact strength of squeeze cast of hybrid metal matrix (LM24–SiC p–coconut shell ash) composite. J Braz Soc Mech Sci Eng 40(1):2
Ma X, Chang PR, Yang J, Yu J (2009) Preparation and properties of glycerol plasticized-pea starch/zinc oxide-starch bionanocomposites. Carbohyd Polym 75(3):472–478. https://doi.org/10.1016/j.carbpol.2008.08.007
Tun KS, Jayaramanavar P, Nguyen QB, Chan J, Kwok R, Gupta M (2012) Investigation into tensile and compressive responses of Mg–ZnO composites. Mater Sci Technol 28(5):582–588
Selvam B, Marimuthu P, Narayanasamy R, Anandakrishnan V, Tun KS, Gupta M, Kamaraj M (2014) Dry sliding wear behaviour of zinc oxide reinforced magnesium matrix nano-composites. Mater Des 58:475–481. https://doi.org/10.1016/j.matdes.2014.02.006
Jasim AH, Joudi WM, Radhi NS, Saud AN (2020) Mechanical properties and wear characteristic of (aluminum-zinc oxide) metal matrix composite prepared using stir casting process. Mater Sci Forum 1002:175–184. Trans Tech Publications Ltd
Raju RSS, Panigrahi MK, Ganguly RI, Rao GS (2017) Investigation of tribological behavior of a novel hybrid composite prepared with Al-coconut shell ash mixed with graphite. Metall Mater Trans A 48(8):3892–3903. https://doi.org/10.1007/s11661-017-4139-1
Tang F, Wu X, Ge S, Ye J, Zhu H, Hagiwara M, Schoenung JM (2008) Dry sliding friction and wear properties of B4C particulate-reinforced Al-5083 matrix composites. Wear 264(7–8):555–561. https://doi.org/10.1016/j.wear.2007.04.006
Thiyaneshwaran N, Sureshkumar P (2013) Microstructure, mechanical and wear properties of aluminum 5083 alloy processed by equal channel angular extrusion. Int J Eng Res Technol 2:17–24
Bathula S, Saravanan M, Dhar A (2012) Nanoindentation and wear characteristics of Al 5083/SiCp nanocomposites synthesized by high energy ball milling and spark plasma sintering. J Mater Sci Technol 28(11):969–975
Madakson PB, Yawas DS, Apasi A (2012) Characterization of coconut shell ash for potential utilization in metal matrix composites for automotive applications. Int J Eng Sci Technol 4(3):1190–1198
Daramola OO, Adediran AA, Fadumiye AT (2015) Evaluation of the mechanical properties and corrosion behaviour of coconut shell ash reinforced aluminium (6063) alloy composites. Leonardo Electron J Pract Technol 27:107–119
Agunsoyea JO, Talabib SI, Belloa SA, Awec IO (2014) The effects of Cocos Nucifera (coconut shell) on the mechanical and tribological properties of recycled waste aluminium can composites. Tribol Industry 36(2)
Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T (2016) Accelerated search for materials with targeted properties by adaptive design. Nat Commun 7(1):1–9. https://doi.org/10.1038/ncomms11241
Xiong J, Shi SQ, Zhang TY (2020) A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater Des 187:108378. https://doi.org/10.1016/j.matdes.2019.108378
Sarica A, Cerasa A, Quattrone A (2017) Random Forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front Aging Neurosci 9:329. https://doi.org/10.3389/fnagi.2017.00329
Abd Jalil K, Kamarudin MH, Masrek MN (2010) Comparison of machine learning algorithms performance in detecting network intrusion. In: 2010 international conference on networking and information technology. IEEE, pp 221–226
Tretyakov K (2004) Machine learning techniques in spam filtering. In: Data mining problem-oriented seminar, MTAT, vol 3, No 177, pp 60–79. Citeseer
Shataee S, Kalbi S, Fallah A, Pelz D (2012) Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. Int J Remote Sens 33(19):6254–6280. https://doi.org/10.1080/01431161.2012.682661
Nagaraj A, Gopalakrishnan S (2021) A study on mechanical and tribological properties of aluminium 1100 alloys 6% of RHAp, BAp, CSAp, ZnOp and egg shellp composites by ANN. SILICON 13(10):3367–3376
Mazahery A, Shabani MO (2012) Study on microstructure and abrasive wear behavior of sintered Al matrix composites. Ceram Int 38(5):4263–4269
Alizadeh A, Abdollahi A, Biukani H (2015) Creep behavior and wear resistance of Al 5083 based hybrid composites reinforced with carbon nanotubes (CNTs) and boron carbide (B4C). J Alloy Compd 650:783–793
Zhao Q, Liang Y, Zhang Z, Li X, Ren L (2016) Microstructure and dry-Sliding wear behavior of B4C ceramic particulate reinforced Al 5083 matrix composite. Metals 6(9):227
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Nagaraj, A., Gopalakrishnan, S., Sakthivel, M., Shivalingappa, D. (2024). Prediction of Tribological Behaviour of AA5083/CSA-ZnO Hybrid Composites Using Machine Learning and Artificial Intelligence Techniques. In: Boppana, S.B., Ramachandra, C.G., Kumar, K.P., Ramesh, S. (eds) Structural Composite Materials. Composites Science and Technology . Springer, Singapore. https://doi.org/10.1007/978-981-99-5982-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-5982-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5981-5
Online ISBN: 978-981-99-5982-2
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)