Wear and Frictional Behaviour of Metals

Alotaibi, J. G.; Yousif, B. F.; Yusaf, Talal

doi:10.4028/www.scientific.net/amr.893.430

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Aluminium, being a light metal, was influenced at smaller loads. Previous studies and literature review suggest that the fluctuations in such frictional behavior occur because of transfer of materials from one surface to another (Sahin et al, 2007;Alidokht et al, 2012;Alotaibi et al, 2014a). Comparing the frictional values for all three metals, at 30N applied load, it is found that copper exhibit the highest friction coefficient than others.…”

Section: Resultsmentioning

confidence: 79%

Tribological Characteristics of Commercial Metals

Alajmi¹,

Ashwin²,

Alotaibi³

et al. 2020

RJASET

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This project conducts a study on wear performance and frictional behaviour of selected metals against stainless steel counterface under dry contact condition. The chosen materials for conducting this study are mild steel, copper and aluminium. The parameters used for inspection and analysis of this project are applied load (0-90N) and sliding distance (0-14 km). Block on ring machine was used to conduct the adhesive wear testings. The worn surfaces are examined and wear mechanisms are categorized using scanning electron microscopy. The results reveal that copper shows better wear properties and aluminium shows less friction. Mild steel exhibits a high rate of wear and material removal. All three materials revealed three different wear mechanisms; aluminium (abrasive and adhesive), mild steel (abrasive and ploughing), copper (adhesive).

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Section: Resultsmentioning

confidence: 79%

Tribological Characteristics of Commercial Metals

Alajmi¹,

Ashwin²,

Alotaibi³

et al. 2020

RJASET

Self Cite

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show abstract

“…The complexity of predicting the wear and frictional performance of the materials motivates the tribologist to adopt an artificial neural network (ANN) approach, as it can be used to make such predictions with caution [7]. With the requirement for accuracy in modelling the behaviours of lubricants becoming more crucial, a sophisticated constitutive model needs to be developed.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural network modelling was employed by Idoko et al, [8] to predict Viscosity index and specific heat capacity of grease lubricant produced from selected oil seeds. Jasem Alotaibi [7], examined the accuracy and prediction performance of artificial neural networks (ANNs) for friction, wear, interface temperature and roughness of metal/metal adhesive wear.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

Bharadwaj

Jeevan²,

Suvin

et al. 2019

AMM

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Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

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Wear and Frictional Behaviour of Metals

Cited by 2 publications

References 12 publications

Tribological Characteristics of Commercial Metals

Tribological Characteristics of Commercial Metals

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

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