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Research Article | Open Access

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

Alexander KOVALEV, Yu TIAN, Yonggang MENG( )
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China
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Graphical Abstract

Abstract

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.

Keywords

machine learningdeep learningartificial neural networkmixed lubrication

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Friction
Volume 12 Issue 6,
June 2024
Pages 1235-1249
DOI: 10.1007/s40544-023-0803-1
Cite this article:
KOVALEV A, TIAN Y, MENG Y. Prediction of ball-on-plate friction and wear by ANN with data-driven optimization. Friction, 2024, 12(6): 1235-1249. https://doi.org/10.1007/s40544-023-0803-1

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Received: 05 April 2023
Revised: 15 May 2023
Accepted: 07 July 2023
Published: 10 January 2024
© The author(s) 2023.

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