Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Prasanth, I.S.N.V.R.; Jeevanandam, Prabahar; Selvaraju, P.; Sathish, K.; Ahammad, S. K. Hasane; Sujatha, P.; Kaarthik, M.; Mayakannan, S.; Sasikumar, Bashyam

doi:10.1155/2023/5723730

Cited by 12 publications

(2 citation statements)

References 43 publications

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“…Te authors [26] found that as the ZrO 2 content of the composites with an Al matrix increased, the porosity of the composites also increased. Te reports show less than 1.5% overall porosity [27]. Te density fndings of this investigation are found to be compatible with the literature.…”

Section: Experimental Densitysupporting

confidence: 90%

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Roy

Shanmugam

Vinothini

et al. 2023

Journal of Chemistry

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This research created hot-pressed composites of the AA6063 matrix with varying concentrations of ZrO2 (0.25, 0.5, and 1 wt %). At sliding speeds of 80, 120, and 150 mm/s, the wear performance of the specimen was studied at loads of 10 N, 15 N, 20 N, and 25 N. The authors analyzed the counter-face material, the wear debris, and the worn surfaces to learn about the wear mechanisms. Developing these three machine learning (ML) algorithms was to evaluate the ability to predict wear behavior using the same small dataset collected using varying test processes. A thorough examination of each model hyperparameter tuning phase was performed. The predictive performance was analyzed using several statistical tools. The most effective decision-making algorithms for this data collection were those based on trees. Predictions made by the decision tree algorithm for the test and validation measurements have an accuracy of 86% and 99.7%, respectively. The best model was picked out based on the results of the predictions.

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Section: Experimental Densitysupporting

confidence: 90%

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Roy

Shanmugam

Vinothini

et al. 2023

Journal of Chemistry

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“…Nosonovsky et al [21] explored a topological data analysis and various ML algorithms, applying them to various aspects of friction data analysis and friction instability. Prasanth et al [27] predicted the COF of AA7075-graphene nanocomposites using five ML regression models. Kolev et al [28] investigated the tribological behavior of porous AlSi10Mg-SiC composites and used three different ML models to predict the COF of the composite materials, evaluating their performance on validation and test sets using R 2 , MSE, RMSE, and MAE metrics.…”

Section: Introductionmentioning

confidence: 99%

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Wang,

Nie,

Liu

et al. 2024

Metals

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To address the problem of tribological failure in an aircraft piston pump valve plate pair, the friction and wear properties of the valve plate pair materials (W9Mo3Cr4V-HAl61-4-3-1) of an axial piston pump at a certain speed and load were studied using a disc-ring tester under lubrication with No. 15 aviation hydraulic oil. The results show that the friction coefficient (COF) fluctuated in the range of 0.019~0.120 when the load (L) increased from 30 N to 120 N, and the speed increased from 100 r/min to 500 r/min. With the increase in the rotational speed, the COF of the valve plate pair decreased first and then increased. When the rotation speed (V) was 300 r/min, the relative COF was the smallest. Under L lower than 60 N, abrasive wear was the main wear mechanism. Under L higher than 90 N, the main wear mechanism was adhesive wear but mild oxidation wear also occurred. In addition, based on the V, L, radius (R), and test duration (T), which affected COF, the random forest regression (RFR) algorithm, the bagging regression (BR) algorithm, and the extra trees regression (ETR) algorithm were used as machine learning methods to predict the COF of the valve plate pair. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to evaluate its performance, with the results showing that the ETR prediction model was the best method for predicting COF. The results of the machine learning also showed that the contributions of V, L, R, and T were 43.56%, 36.76%, 13.13%, and 6.55%, respectively, indicating that V had the greatest influence on the COF of the W9Mo3Cr4V/HAl61-4-3-1 friction pair. This study is expected to provide support for the rapid development of new valve plate pair materials.

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Design and Analysis of Effective Automotive Silencer

Magibalan,

Jagadeesh

2024

Lecture Notes in Mechanical Engineering

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Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Cited by 12 publications

References 43 publications

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Design and Analysis of Effective Automotive Silencer

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