Physics-Informed Machine Learning—An Emerging Trend in Tribology
Abstract
:1. Artificial Intelligence and Machine Learning in Tribology
2. Physics-Informed Machine Learning
3. Physics-Informed Machine Learning in Tribology
3.1. Lubrication Prediction
3.2. Wear and Damage Prediction
4. Concluding Remarks
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Field of Application | PIML Approach | Year | Reference |
---|---|---|---|
Lubrication prediction | Using PINN to solve the 1D Reynolds BVP to predict the pressure distribution in a fluid-lubricated linear converging slider | 2021 | [64] |
Using PINN to solve the 2D Reynolds equation to predict the pressure and film thickness distribution considering load balance in a fluid-lubricated linear converging slider | 2023 | [66] | |
Using supervised, semi-supervised, and unsupervised PINN to solve the 2D Reynolds equation to predict the pressure and film thickness distribution considering load balance and eccentricity in a gas-lubricated journal bearing | 2022 | [68] | |
Using PINN to solve the 2D Reynolds equation to predict the behavior of fluid-lubricated journal as well as two-lobe bearings | 2023 | [69] | |
Using PINN with soft and hard constraints to solve the 2D Reynolds equation to predict the pressure distribution in fluid-lubricated journal bearings at fixed eccentricity with constant and variable viscosity | 2023 | [70] | |
Using PINN to solve the 2D Reynolds equation to predict the pressure and fractional film content distribution in fluid-lubricated journal bearings at fixed and variable eccentricity considering cavitation | 2023 | [71] | |
Using PINN to solve the 2D Reynolds equation to predict the pressure and fractional film content distribution in fluid-lubricated journal bearings at fixed eccentricity considering cavitation | 2023 | [72] | |
Wear and damage prediction | Using semi PINN to find regression fitting parameters for Archard’s wear law based upon small data from fretting wear experiments | 2015 | [77] |
Using hybrid PINN to predict wind turbine bearing fatigue based upon a physics-informed bearing damage model as well as data-driven grease degradation approach | 2020 | [78] | |
Using physics-informed CNN with preceding threshold model for rolling bearing fault detection | 2021 | [79] | |
Using physics-informed residual network for rolling bearing fault detection | 2023 | [80] | |
Using PIML framework consisting of piecewise fitting, a hybrid physics-informed data-driven model, and meta-learning to predict tool wear | 2022 | [81] |
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Marian, M.; Tremmel, S. Physics-Informed Machine Learning—An Emerging Trend in Tribology. Lubricants 2023, 11, 463. https://doi.org/10.3390/lubricants11110463
Marian M, Tremmel S. Physics-Informed Machine Learning—An Emerging Trend in Tribology. Lubricants. 2023; 11(11):463. https://doi.org/10.3390/lubricants11110463
Chicago/Turabian StyleMarian, Max, and Stephan Tremmel. 2023. "Physics-Informed Machine Learning—An Emerging Trend in Tribology" Lubricants 11, no. 11: 463. https://doi.org/10.3390/lubricants11110463