Artificial neural networks applied to the analysis of performance and wear resistance of binary coatings Cr3C237WC18M and WC20Cr3C27Ni

Becker, Anderson; Fals, Hipólito Carvajal; Roca, Ángel Sánchez; Siqueira, Irene B.A.F.; Caliari, Felipe Rocha; Cruz, Juliane Ribeiro da; Váz, Rodolpho Fernando; Sousa, Milton J. de; Pukasiewicz, Anderson Geraldo Marenda

doi:10.1016/j.wear.2021.203797

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…by using a microphone and semi-supervised machinelearning algorithm implemented into recurrent NN. A simple ANN approach based on the "universal approximation" has also been applied to predictions of fretting wear life [434], of surfactant concentration effects in electroless Ni-B coating [435], of wear resistance of binary coatings of Cr3C237WC18M and WC20Cr3C27Ni [436], and identification of 3D ferrography wear particle images [437,438]. Prost et al [439] conducted semi-supervised classification of the state of operation in self-lubricating journal bearings using a random forest classifier.…”

Section: Big Data Machine Learning (Ml) and Artificial Neural Network...mentioning

confidence: 99%

A review of advances in tribology in 2020–2021

Meng

et al. 2022

Friction

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Around 1,000 peer-reviewed papers were selected from 3,450 articles published during 2020–2021, and reviewed as the representative advances in tribology research worldwide. The survey highlights the development in lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology, providing a show window of the achievements of recent fundamental and application researches in the field of tribology.

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Section: Big Data Machine Learning (Ml) and Artificial Neural Network...mentioning

confidence: 99%

A review of advances in tribology in 2020–2021

Meng

et al. 2022

Friction

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“…An aptitude of a neural network system can provide a non-linear relationship between input parameters and output data, which is able to model the operation of mechanical systems. Recently, the ANN technique has very often been used as a tool for the analysis of wear test data [21][22][23][24][25][26]. Many dependencies have been established in the form of ANNs, where the material or mechanical properties [25][26][27], structural or surface parameters [28,29] are used as input data for ANN [6,30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of ball-on-plate friction and wear by ANN with data-driven optimization

Kovalev,

Tian,

Meng

2024

Friction

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For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to “learn” the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.

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“…Linear and nonlinear regression models have been used to establish a relationship between process variables and coating properties [18,24,30,33], while computational fluid dynamics (CFD) models have been utilized to simulate the gas flow, heat transfer, and particle behavior in the spray gun [12,19]. In addition, artificial neural networks (ANNs) [4,20,21,37] and genetic algorithms (GAs) [17] have been implemented to optimize process conditions and predict coating properties.…”

Section: Introductionmentioning

confidence: 99%

Predictive modelling of critical variables for improving HVOF coating using gamma regression models

Rannetbauer,

Hubmer,

Hambrock

et al. 2024

J.Math.Industry

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Thermal spray coating is a critical process in many industries, involving the application of coatings to surfaces to enhance their functionality. This paper proposes a framework for modelling and predicting critical target variables in thermal spray coating processes, based on the application of statistical design of experiments (DoE) and the modelling of the data using generalized linear models (GLMs) with a particular emphasis on gamma regression. Experimental data obtained from thermal spray coating trials are used to validate the presented approach, demonstrating that it is able to accurately model and predict critical target variables. As such, the framework has significant potential for the optimization of thermal spray coating processes, and can contribute to the development of more efficient and effective coating technologies in various industries.

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Artificial neural networks applied to the analysis of performance and wear resistance of binary coatings Cr3C237WC18M and WC20Cr3C27Ni

Cited by 19 publications

References 24 publications

A review of advances in tribology in 2020–2021

A review of advances in tribology in 2020–2021

Prediction of ball-on-plate friction and wear by ANN with data-driven optimization

Predictive modelling of critical variables for improving HVOF coating using gamma regression models

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