Lubricants | Hot Papers about Machine Learning and Artificial Intelligence in Tribology
The integration of machine learning and artificial intelligence (AI) into the field of tribology marks a significant stride toward enhancing our understanding and control of friction, wear, and lubrication phenomena in mechanical systems. Tribology, the science of interacting surfaces in relative motion, plays a crucial role in numerous industrial applications, including manufacturing, transportation, and energy systems. By leveraging machine learning algorithms and AI techniques, researchers are empowered to analyze vast datasets, model complex interactions, and predict tribological behavior with unprecedented precision. These technologies offer the potential to optimize lubrication strategies, predict component wear, and design more durable materials. The marriage of machine learning and tribology not only revolutionizes our ability to comprehend intricate frictional processes but also opens new avenues for innovation in the development of intelligent systems that can autonomously adapt to changing tribological conditions, leading to more efficient and reliable machinery across diverse sectors. Engaging with papers on this intersection between machine learning, artificial intelligence, and tribology provides a glimpse into the forefront of research, where advanced computational methods are reshaping our approach to mitigating friction and enhancing the performance and lifespan of mechanical components.
1. “Physics-Informed Machine Learning—An Emerging Trend in Tribology”
by Max Marian and Stephan Tremmel
Lubricants 2023, 11(11), 463; https://doi.org/10.3390/lubricants11110463
Available online: https://www.mdpi.com/2075-4442/11/11/463
2. “Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry”
by Md Hafizur Rahman, Sadat Shahriar and Pradeep L. Menezes
Lubricants 2023, 11(7), 289; https://doi.org/10.3390/lubricants11070289
Available online: https://www.mdpi.com/2075-4442/11/7/289
3. “An AI-Extended Prediction of Erosion-Corrosion Degradation of API 5L X65 Steel”
by Ariel Espinoza-Jara, Igor Wilk, Javiera Aguirre and Magdalena Walczak
Lubricants 2023, 11(10), 431; https://doi.org/10.3390/lubricants11100431
Available online: https://www.mdpi.com/2075-4442/11/10/431
4. “Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts”
by Joe Issa, Alain El Hajj, Philippe Vergne and Wassim Habchi
Lubricants 2023, 11(12), 497; https://doi.org/10.3390/lubricants11120497
Available online: https://www.mdpi.com/2075-4442/11/12/497
5. “Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment”
by Ali Usman, Saad Arif, Ahmed Hassan Raja, Reijo Kouhia, Andreas Almqvist and Marcus Liwicki
Lubricants 2023, 11(6), 254; https://doi.org/10.3390/lubricants11060254
Available online: https://www.mdpi.com/2075-4442/11/6/254
6. “Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines”
by Christian Laubichler, Constantin Kiesling, Matheus Marques da Silva, Andreas Wimmer and Gunther Hager
Lubricants 2022, 10(5), 103; https://doi.org/10.3390/lubricants10050103
Available online: https://www.mdpi.com/2075-4442/10/5/103
7. “A New Approach for the Tribological and Mechanical Characterization of a Hip Prosthesis Trough a Numerical Model Based on Artificial Intelligence Algorithms and Humanoid Multibody Model”
by Dario Milone, Giacomo Risitano, Alessandro Pistone, Davide Crisafulli and Fabio Alberti
Lubricants 2022, 10(7), 160; https://doi.org/10.3390/lubricants10070160
Available online: https://www.mdpi.com/2075-4442/10/7/160
8. “Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing”
by Sebastian Schwarz, Hannes Grillenberge, Oliver Graf-Goller, Marcel Bartz, Stephan Tremmel and Sandro Wartzack
Lubricants 2022, 10(2), 25; https://doi.org/10.3390/lubricants10020025
Available online: https://www.mdpi.com/2075-4442/10/2/25
9. “Prediction of Friction Power via Machine Learning of Acoustic Emissions from a Ring-on-Disc Rotary Tribometer”
by Christopher Strablegg, Florian Summer, Philipp Renhart and Florian Grün
Lubricants 2023, 11(2), 37; https://doi.org/10.3390/lubricants11020037
Available online: https://www.mdpi.com/2075-4442/11/2/37
10. “Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques”
by Konstantinos P. Katsaros and Pantelis G. Nikolakopoulos
Lubricants 2023, 11(3), 113; https://doi.org/10.3390/lubricants11030113
Available online: https://www.mdpi.com/2075-4442/11/3/113