Soft computing model for predicting the wear resistance of friction stir processed aluminum alloy AA5083

Kumar, K. Ashok; Vignesh, R. Vaira; Kumar, G. Ajith; Abiram, A.; Padmanaban, R.

doi:10.1016/j.matpr.2020.12.472

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…ANNs have strong adaptation capabilities in many model establishments of complex problems and are used in various fields [16]. Many researchers use ANNs for the prediction of wear, wear resistance, wear depth, wear rate, and flank wear [38][39][40][41]. Hanief [8] established an ANN model to research the modification of the surface morphology roughness over time during the running-in wear period.…”

Section: Annmentioning

confidence: 99%

Optimization of running-in surface morphology parameters based on the AutoML model

Liu

Zhang

2021

PLoS ONE

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Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing, and the surface morphology optimization is explored. When better oil storage capacity is required, the recommendation is to increase the Sq, Sdq and Sk surface parameter values while setting medium Sdc and Sdr surface parameter values. When a lower coefficient of friction (COF) is required, Sdc and Sdr should be decreased, and Sq and Sdq should be increased. When better support performance is required, Sdc, Sdq, and Sdr should be increased. This article provides a solution to establish a link between surface design and functional performance in the steady wear stage, further filling the gap in quality monitoring of lifecycles.

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Section: Annmentioning

confidence: 99%

Optimization of running-in surface morphology parameters based on the AutoML model

Liu

Zhang

2021

PLoS ONE

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“…This artificial based modeling approaches overcomes the problems of conventional mathematical modeling techniques as well as numerical modeling techniques such as model complexity and nonlinearity [49,50]. Kumar et al [51] developed an ANN model as well as a fuzzy inference model to predict the wear resistance of FSPS made of AA5083 plates. The hardness of the specimens was enhanced by refining the grains using the FSP technique.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

et al. 2021

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In the current investigation, AA2024 aluminum alloy is reinforced by alumina nanoparticles using a friction stir process (FSP) with multiple passes. The mechanical properties and microstructure observation are conducted experimentally using tensile, microhardness, and microscopy analysis methods. The impacts of the process parameters on the output responses, such as mechanical properties and microstructure grain refinement, were investigated. The effect of multiple FSP passes on the grain refinement, and various mechanical properties are evaluated, then the results are conducted to train a hybrid artificial intelligence predictive model. The model consists of a multilayer perceptrons optimized by a grey wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by alumina nanoparticles. The inputs of the model were rotational speed, linear processing speed, and number of passes; while the outputs were grain size, aspect ratio, microhardness, and ultimate tensile strength. The prediction accuracy of the developed hybrid model was compared with that of standalone multilayer perceptrons model using different error measures. The developed hybrid model shows a higher accuracy compared with the standalone model.

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Soft computing model for predicting the wear resistance of friction stir processed aluminum alloy AA5083

Cited by 2 publications

References 23 publications

Optimization of running-in surface morphology parameters based on the AutoML model

Optimization of running-in surface morphology parameters based on the AutoML model

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

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