Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network
The present study proposes a Bayesian-optimized back-propagation (BP) neural network framework for predicting the tribological performance of hyaluronic acid (HA) aqueous solutions under hydrodynamic lubrication conditions. The model addresses the complex rheological behavior of HA and limitations o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/13/5/215 |
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| author | Xia Li Feng Guo |
| author_facet | Xia Li Feng Guo |
| author_sort | Xia Li |
| collection | DOAJ |
| description | The present study proposes a Bayesian-optimized back-propagation (BP) neural network framework for predicting the tribological performance of hyaluronic acid (HA) aqueous solutions under hydrodynamic lubrication conditions. The model addresses the complex rheological behavior of HA and limitations of traditional trial-and-error methods. It integrates four operational parameters—applied load, sliding speed, fluid viscosity and contact surface inclination. These enable the simultaneous prediction of two critical lubrication characteristics: film thickness and load-carrying capacity. Bayesian optimization was used to automate hyperparameter tuning. This can significantly improve computational efficiency. The optimized model showed a coefficient of determination (R<sup>2</sup>) of 0.938 and a mean square error (MSE) of 0.0025 on the test dataset, indicating its ability for accurate prediction. The results indicated a significant positive correlation between HA viscosity and lubrication performance. This framework can be used as a screening tool for HA-based lubricants. The integration of machine learning with biotribology may offer opportunities to improve data-driven approaches to analyzing complex fluid behavior, where traditional models have limitations. |
| format | Article |
| id | doaj-art-6dcc73d6ae3c4e59ada34bc70621ff5a |
| institution | OA Journals |
| issn | 2075-4442 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj-art-6dcc73d6ae3c4e59ada34bc70621ff5a2025-08-20T01:56:19ZengMDPI AGLubricants2075-44422025-05-0113521510.3390/lubricants13050215Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP NetworkXia Li0Feng Guo1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaDepartment of Vehicle Engineering, Yantai Automobile Engineering Professional College, Yantai 265599, ChinaThe present study proposes a Bayesian-optimized back-propagation (BP) neural network framework for predicting the tribological performance of hyaluronic acid (HA) aqueous solutions under hydrodynamic lubrication conditions. The model addresses the complex rheological behavior of HA and limitations of traditional trial-and-error methods. It integrates four operational parameters—applied load, sliding speed, fluid viscosity and contact surface inclination. These enable the simultaneous prediction of two critical lubrication characteristics: film thickness and load-carrying capacity. Bayesian optimization was used to automate hyperparameter tuning. This can significantly improve computational efficiency. The optimized model showed a coefficient of determination (R<sup>2</sup>) of 0.938 and a mean square error (MSE) of 0.0025 on the test dataset, indicating its ability for accurate prediction. The results indicated a significant positive correlation between HA viscosity and lubrication performance. This framework can be used as a screening tool for HA-based lubricants. The integration of machine learning with biotribology may offer opportunities to improve data-driven approaches to analyzing complex fluid behavior, where traditional models have limitations.https://www.mdpi.com/2075-4442/13/5/215hyaluronic acid (HA)BP neural networkBayesian optimizationfilm thicknessload-carrying capacity |
| spellingShingle | Xia Li Feng Guo Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network Lubricants hyaluronic acid (HA) BP neural network Bayesian optimization film thickness load-carrying capacity |
| title | Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network |
| title_full | Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network |
| title_fullStr | Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network |
| title_full_unstemmed | Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network |
| title_short | Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network |
| title_sort | prediction of lubrication performance of hyaluronic acid aqueous solutions using a bayesian optimized bp network |
| topic | hyaluronic acid (HA) BP neural network Bayesian optimization film thickness load-carrying capacity |
| url | https://www.mdpi.com/2075-4442/13/5/215 |
| work_keys_str_mv | AT xiali predictionoflubricationperformanceofhyaluronicacidaqueoussolutionsusingabayesianoptimizedbpnetwork AT fengguo predictionoflubricationperformanceofhyaluronicacidaqueoussolutionsusingabayesianoptimizedbpnetwork |