The use of artificial neural network for the prediction of wear loss of aluminium-magnesium alloys

Hariprasad, T.; Shivalingappa, D.; Nagaraj, A.; Manivasagam, Geetha

doi:10.1504/ijcaet.2015.066174

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Due to weak bonding, the particles along with Mg powder get ploughed away in the intent of load application and velocity leading to an intense wear of the specimen. It can be seen from figure 6 that the coefficient of friction value decreases with increase in r-GO which can further be accredited to the self-lubricating property of r-GO layers [20]. Beyond this, it continues to provide a self-lubricating layer between the pin-disc interfaces and thereby reduces the friction created by the pin over the disc counter-face and by this means also reduces the coefficient of friction.…”

Section: 3a Wear Characterizationmentioning

confidence: 90%

Investigation of graphene-reinforced magnesium metal matrix composites processed through a solvent-based powder metallurgy route

2019

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In the present investigation, AZ31 alloy is homogeneously reinforced with 0.2, 0.3, 0.4 and 0.5 wt% of reduced graphene oxide (r-GO) nanosheets for the first time through a series of methodologies involving solvent processing, mechanical alloying, cold pressing and finally sintering under argon atmosphere at 560 • C. Scanning Electron Microscopy (SEM) assisted with energy-dispersive X-ray analysis revealed that this inventive fabrication route is useful to easily disperse r-GO into the matrix material and thereby attain methodical homogeneity with a uniform particle size. The attained results show that amongst the others, addition of 0.4 wt% r-GO have obviously improved the hardness up to 64.6 HV and also yielded a better inhibition efficiency of 84% on corrosion. Any further increase of r-GO content resulted for significant decrease in the wear rate up to the level of 2.6 mm 3 Nm −1 .

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Section: 3a Wear Characterizationmentioning

confidence: 90%

Investigation of graphene-reinforced magnesium metal matrix composites processed through a solvent-based powder metallurgy route

2019

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“…The numbers of training data are taken approximately to be equal to 80% of the total data, while the numbers of testing data are taken randomly to be approximately equal to 20% of the total database. The development of ANN requires the selection of number of hidden layers and the neurons in each of hidden layer from the best activation function, the trial and error have been carried out to choose the best number of hidden layers and the number of neurons in each hidden layer [21].…”

Section: Neural Network Model Development and Optimizationmentioning

confidence: 99%

Artificial Neural Network Model for Estimation of Wear and Temperature in Pin-disc Contact

Hassan¹,

Mohammed²

2016

ujme

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This work aims to investigate experimentally the parameters affecting on the wear debris and the temperature rise due to friction as well as developing the artificial neural network model (ANN) using MATLAB program for predicting the wear, and temperature of disc and pad. Two types of disc made from aluminum and steel are slipping against pad and carried out under dry conditions at different time, rotational speed, and load to examine the wear. The results show that the wear and temperature are increased with increasing the sliding speed, and load or contact time. In addition, the wear of pad is higher when it's contact with aluminum disc, while the temperature of pad is higher when its contact with steel. The ANN model was successfully shows that there is a high ability to predict the wear and temperature as well as the results of model corresponding with the experimental results.

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“…Additionally, a simple model of linear regression (single-variable) was deployed by Gailis et al, who used the mileage of the vehicle as the only variable in the model to predict the brake wear out [199]. In 2006, Durmuş et al investigated the rate of wear loss and surface roughness of an aluminum alloy by using a model based on the artificial neural network [200], and similar studies have been undertaken in [201][202][203]. A neural model of brake wear prediction was developed by Aleksendrić based on the complete formulation of the friction materials [204].…”

Section: Simulation Methodologiesmentioning

confidence: 99%

Input Parameters for Airborne Brake Wear Emission Simulations: A Comprehensive Review

2021

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Non-exhaust emissions, generated by the wear of brake systems, tires, roads, clutches, and road resuspension, are responsible for a large part of airborne pollutants in urban areas. Brake wear accounts for 55% of non-exhaust emissions and significantly contributes to urban health diseases related to air pollution. A major part of the studies reported in the scientific literature are focused on experimental methods to sample and characterize brake wear particles in a reliable, representative, and repeatable way. In this framework, simulation is an important tool, which makes it possible to give interpretations of the experimental results, formulate new testing approaches, and predict the emission produced by brakes. The present comprehensive literature review aims to introduce the state of the art of the research on the different aspects of airborne wear debris resulting from brake systems which can be used as inputs in future simulation models. In this review, previous studies focusing on airborne emissions produced by brake systems are investigated in three main categories: the subsystem level, system level, and environmental level. As well as all the information provided in the literature, the simulation methodologies are also investigated at all levels. It can be concluded from the present review study that various factors, such as the uncertainty and repeatability of the brake wear experiments, distinguish the results of the subsystem and system levels. This gap should be taken into account in the development of future experimental and simulation methods for the investigation of airborne brake wear emissions.

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The use of artificial neural network for the prediction of wear loss of aluminium-magnesium alloys

Cited by 5 publications

References 4 publications

Investigation of graphene-reinforced magnesium metal matrix composites processed through a solvent-based powder metallurgy route

Investigation of graphene-reinforced magnesium metal matrix composites processed through a solvent-based powder metallurgy route

Artificial Neural Network Model for Estimation of Wear and Temperature in Pin-disc Contact

Input Parameters for Airborne Brake Wear Emission Simulations: A Comprehensive Review

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