Ning Jiang , Rundong Qian , Haiyu Qiao , Yani Chen , Honghui Cao , Yayun Liu , Chuanyang Wang
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引用次数: 0
Abstract
This study presents a comprehensive approach to enhancing RV reducer performance. A novel RAM viscosity-pressure model is developed, combining traditional stability with neural network precision. Surface micro-morphological analysis is integrated with contact fatigue modeling, revealing critical insights into surface characteristics and their impact on fatigue. A neural proxy-based optimization strategy is employed to identify optimal operational parameters, significantly reducing fatigue risks. Additionally, a new method for real-time transmission ratio monitoring is introduced, validating the model's effectiveness and offering a robust framework for improving the reliability and lifespan of RV reducers in industrial applications.
期刊介绍：
Tribology is the science of rubbing surfaces and contributes to every facet of our everyday life, from live cell friction to engine lubrication and seismology. As such tribology is truly multidisciplinary and this extraordinary breadth of scientific interest is reflected in the scope of Tribology International.
Tribology International seeks to publish original research papers of the highest scientific quality to provide an archival resource for scientists from all backgrounds. Written contributions are invited reporting experimental and modelling studies both in established areas of tribology and emerging fields. Scientific topics include the physics or chemistry of tribo-surfaces, bio-tribology, surface engineering and materials, contact mechanics, nano-tribology, lubricants and hydrodynamic lubrication.
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