|
|
|
|
|
文献清单：“机器学习与人工智能在摩擦学领域的应用”方向 | MDPI Lubricants |
|
|
期刊名：Lubricants
期刊主页：https://www.mdpi.com/journal/lubricants
在这个科技日新月异的时代，机器学习与人工智能正以前所未有的速度渗透到各行各业，引领着技术革命的新浪潮。本期文献清单，我们特别精选关于机器学习与人工智能在摩擦学领域的研究成果，为您研究摩擦、磨损及润滑等问题提供有一些全新的思路与方法。
1.
英文标题:
Current Trends and Applications of Machine Learning in Tribology—A Review
中文标题:
机器学习在摩擦学中的发展趋势及应用—综述
文章链接：https://www.mdpi.com/2075-4442/9/9/86
MDPI引用格式:
Marian, M.; Tremmel, S. Current Trends and Applications of Machine Learning in Tribology—A Review. Lubricants 2021, 9, 86.
2.
英文标题:
The Use of Artificial Intelligence in Tribology—A Perspective
中文标题:
人工智能在摩擦学中的应用—观点
文章链接：https://www.mdpi.com/2075-4442/9/1/2
MDPI引用格式:
Rosenkranz, A.; Marian, M.; Profito, F.J.; Aragon, N.; Shah, R. The Use of Artificial Intelligence in Tribology—A Perspective. Lubricants 2021, 9, 2.
3.
英文标题:
Physics-Informed Machine Learning—An Emerging Trend in Tribology
中文标题:
物理信息驱动的机器学习—摩擦学领域的新趋势
文章链接：https://www.mdpi.com/2075-4442/11/11/463
MDPI引用格式:
Marian, M.; Tremmel, S. Physics-Informed Machine Learning—An Emerging Trend in Tribology. Lubricants 2023, 11, 463.
4.
英文标题:
Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier
中文标题:
基于随机森林分类器的自润滑滑动轴承运行状态半监督分类
文章链接：https://www.mdpi.com/2075-4442/9/5/50
MDPI引用格式:
Prost, J.; Cihak-Bayr, U.; Neac?u, I.A.; Grundtner, R.; Pirker, F.; Vorlaufer, G. Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier. Lubricants 2021, 9, 50.
5.
英文标题:
On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor
中文标题:
滚动轴承剩余使用寿命预测中时间信息的重要性—基于随机森林回归器
文章链接：https://www.mdpi.com/2075-4442/10/4/67
MDPI引用格式:
Bienefeld, C.; Kirchner, E.; Vogt, A.; Kacmar, M. On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor. Lubricants 2022, 10, 67.
6.
英文标题:
Lubrication Regime Classification of Hydrodynamic Journal Bearings by Machine Learning Using Torque Data
中文标题:
基于机器学习的流体动压滑动轴承润滑状态分类—使用扭矩数据
文章链接：https://www.mdpi.com/2075-4442/6/4/108
MDPI引用格式:
Moder, J.; Bergmann, P.; Grün, F. Lubrication Regime Classification of Hydrodynamic Journal Bearings by Machine Learning Using Torque Data. Lubricants 2018, 6, 108.
7.
英文标题:
Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network
中文标题:
基于贝叶斯优化BP网络的透明质酸水溶液润滑性能预测
文章链接：https://www.mdpi.com/2075-4442/13/5/215
MDPI引用格式:
Li, X.; Guo, F. Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network. Lubricants 2025, 13, 215.
8.
英文标题:
Braking Friction Coefficient Prediction Using PSO–GRU Algorithm Based on Braking Dynamometer Testing
中文标题:
基于制动测试仪的PSO–GRU算法制动摩擦系数预测
文章链接：https://www.mdpi.com/2075-4442/12/6/195
MDPI引用格式:
Wang, S.; Yu, Y.; Liu, S.; Barton, D. Braking Friction Coefficient Prediction Using PSO–GRU Algorithm Based on Braking Dynamometer Testing. Lubricants 2024, 12, 195.
9.
英文标题:
Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks
中文标题:
利用前馈神经网络研究船用馏分柴油金属离子浓度的机器学习方法
文章链接：https://www.mdpi.com/2075-4442/12/4/127
MDPI引用格式:
Savvides, A.-A.; Papadopoulos, L.; Intzirtzis, G.; Kalligeros, S. Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks. Lubricants 2024, 12, 127.
10.
英文标题:
The Prediction of Wear Depth Based on Machine Learning Algorithms
中文标题:
基于机器学习算法的磨损深度预测
文章链接：https://www.mdpi.com/2075-4442/12/2/34
MDPI引用格式:
Zhu, C.; Jin, L.; Li, W.; Han, S.; Yan, J. The Prediction of Wear Depth Based on Machine Learning Algorithms. Lubricants 2024, 12, 34.
11.
英文标题:
Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts
中文标题:
弹性流体润滑椭圆接触中膜厚预测的机器学习
文章链接：https://www.mdpi.com/2075-4442/11/12/497
MDPI引用格式:
Issa, J.; El Hajj, A.; Vergne, P.; Habchi, W. Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts. Lubricants 2023, 11, 497.
12.
英文标题:
Artificial Neural Network-Based Analysis of the Tribological Behavior of Vegetable Oil–Diesel Fuel Mixtures
中文标题:
基于人工神经网络的植物油-柴油混合燃料摩擦学行为分析
文章链接：https://www.mdpi.com/2075-4442/7/4/32
MDPI引用格式:
Humelnicu, C.; Ciortan, S.; Amortila, V. Artificial Neural Network-Based Analysis of the Tribological Behavior of Vegetable Oil–Diesel Fuel Mixtures. Lubricants 2019, 7, 32.
13.
英文标题:
Intelligent Tool Wear Monitoring Method Using a Convolutional Neural Network and an Informer
中文标题:
基于卷积神经网络和信息器的工具磨损智能监测方法
文章链接：https://www.mdpi.com/2075-4442/11/9/389
MDPI引用格式:
Xie, X.; Huang, M.; Sun, W.; Li, Y.; Liu, Y. Intelligent Tool Wear Monitoring Method Using a Convolutional Neural Network and an Informer. Lubricants 2023, 11, 389.
14.
英文标题:
A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning
中文标题:
基于机器学习的表面纹理密封摩擦优化通用方法
文章链接：https://www.mdpi.com/2075-4442/12/1/20
MDPI引用格式:
Brase, M.; Binder, J.; Jonkeren, M.; Wangenheim, M. A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning. Lubricants 2024, 12, 20.
15.
英文标题:
Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques
中文标题:
结合数值和机器学习技术的不完整油膜下流体动力润滑可倾瓦推力轴承性能预测模型
文章链接：https://www.mdpi.com/2075-4442/11/3/113
MDPI引用格式:
Katsaros, K.P.; Nikolakopoulos, P.G. Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques. Lubricants 2023, 11, 113.
16.
英文标题:
Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry
中文标题:
石油和润滑油行业机器学习算法的最新进展
文章链接：https://www.mdpi.com/2075-4442/11/7/289
MDPI引用格式:
Rahman, M.H.; Shahriar, S.; Menezes, P.L. Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry. Lubricants 2023, 11, 289.
17.
英文标题:
Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments
中文标题:
用于摩擦学实验中固定相自动识别的长短期记忆网络
文章链接：https://www.mdpi.com/2075-4442/12/12/423
MDPI引用格式:
Zhao, Y.; Lin, L.; Schlarb, A.K. Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments. Lubricants 2024, 12, 423.
18.
英文标题:
A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM
中文标题:
基于半监督LSTM的刀具磨损识别新方法
文章链接：https://www.mdpi.com/2075-4442/13/2/72
MDPI引用格式:
He, X.; Zhong, M.; He, C.; Wu, J.; Yang, H.; Zhao, Z.; Yang, W.; Jing, C.; Li, Y.; Gao, C. A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM. Lubricants 2025, 13, 72.
Lubricants 期刊介绍
主编：Homer Rahnejat, University of Central Lancashire, UK
期刊致力于为摩擦学领域及相关的学科提供高质量出版平台，主要发表包括摩擦、润滑和磨损等方面科研成果。目前期刊已被SCI (Web of Science)、Scopus、Inspec等数据库收录。
|
2024 Impact Factor
|
2.9
|
|
2024 CiteScore
|
4.5
|
|
Time to First Decision
|
14.6 Days
|
|
Acceptance to Publication
|
2.5 Days
|
特别声明：本文转载仅仅是出于传播信息的需要，并不意味着代表本网站观点或证实其内容的真实性；如其他媒体、网站或个人从本网站转载使用，须保留本网站注明的“来源”，并自负版权等法律责任；作者如果不希望被转载或者联系转载稿费等事宜，请与我们接洽。
