通过数据驱动优化的 ANN 预测球-板摩擦和磨损

IF 6.3 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Friction Pub Date : 2024-01-10 DOI:10.1007/s40544-023-0803-1
Alexander Kovalev, Yu Tian, Yonggang Meng
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引用次数: 0

摘要

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Prediction of ball-on-plate friction and wear by ANN with data-driven optimization
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来源期刊
Friction
Friction Engineering-Mechanical Engineering
CiteScore
12.90
自引率
13.20%
发文量
324
审稿时长
13 weeks
期刊介绍: Friction is a peer-reviewed international journal for the publication of theoretical and experimental research works related to the friction, lubrication and wear. Original, high quality research papers and review articles on all aspects of tribology are welcome, including, but are not limited to, a variety of topics, such as: Friction: Origin of friction, Friction theories, New phenomena of friction, Nano-friction, Ultra-low friction, Molecular friction, Ultra-high friction, Friction at high speed, Friction at high temperature or low temperature, Friction at solid/liquid interfaces, Bio-friction, Adhesion, etc. Lubrication: Superlubricity, Green lubricants, Nano-lubrication, Boundary lubrication, Thin film lubrication, Elastohydrodynamic lubrication, Mixed lubrication, New lubricants, New additives, Gas lubrication, Solid lubrication, etc. Wear: Wear materials, Wear mechanism, Wear models, Wear in severe conditions, Wear measurement, Wear monitoring, etc. Surface Engineering: Surface texturing, Molecular films, Surface coatings, Surface modification, Bionic surfaces, etc. Basic Sciences: Tribology system, Principles of tribology, Thermodynamics of tribo-systems, Micro-fluidics, Thermal stability of tribo-systems, etc. Friction is an open access journal. It is published quarterly by Tsinghua University Press and Springer, and sponsored by the State Key Laboratory of Tribology (TsinghuaUniversity) and the Tribology Institute of Chinese Mechanical Engineering Society.
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