期刊文献+

轴承衬材料和润滑剂在滑动轴承的选择

The Choice of Bearing Lining Materials and Lubricants in Sliding Bearings
下载PDF
导出
摘要 轴承衬材料以及润滑剂作为滑动轴承组成结构的重要构成,在增强滑动轴承的工作稳定性、安全性以及使用寿命等方面发挥着关键性的作用,可以提升轴承的工作运行能力,满足社会经济发展过程中对于滑动轴承工作效率的客观需求。文章全面分析滑动轴承的基本类型,在此基础上,对滑动轴承结构特点、轴承衬材料以及润滑剂的选择类型以及运用方式进行探讨,以期充分发挥滑动轴承在工业生产以及经济发展中的重要作用,推动滑动轴承产业的健康快速发展。 Bearing lining material and lubricant, as an important component of sliding bearing composition structure, play a key role in enhancing the working stability, safety and service life of the sliding bearing, and it can enhance the operation capacity of bearing and meet the objective needs for the working efficiency of sliding bearing in socio-economic development process. This paper analyzes the basic types of sliding bearings, on the basis, discusses the structural characteristics of sliding bearings, the choice of bearing lining materials and lubricant and the way of operation, in order to give full play to the important role of sliding bearings in industrial production and economic development and promote the healthy and rapid development of sliding bearing industry.
作者 闫深
出处 《价值工程》 2017年第27期141-142,共2页 Value Engineering
关键词 滑动轴承 轴承衬材料 润滑剂 选择 sliding bearing bearing lining material lubricant choice
  • 相关文献

参考文献5

二级参考文献42

  • 1曹祝君,吴国凤.一种改进的遗传算法[J].合肥工业大学学报(自然科学版),2004,27(9):1070-1073. 被引量:8
  • 2邱宣怀.机械设计[M].北京:高教教育出版社,1994..
  • 3刘惟信.机械最优化设计[M].2版.北京:清华大学出版社,1994.
  • 4北京钢铁学院,等.机械零件附册[M].北京:人民教育出版社,1980:71.
  • 5Price K V, Storn R M, Lampinen J. Differential evolution: a practical approach to global optimization [M]. Berline, Springer Verlag, 2005: 30.
  • 6Durillo J, Nebro A, Luna F, et al. Solving three-objective optimization problems using a new hybrid cellular genetic algo- rithm [M] //Proceedings of the International Conference on Parallel Problem Solving from Nature. Heidelberg, Berlin: Springer-Verlag. 2008: 661-670.
  • 7Deb K, Pratap A, Meyarivan T. A fast and elitist multi-ob- jective genetic algorithm: NSGA- II [J]. IEEE Transactions on Evolutionary Computation, 200Z, 6 (2): 182-197.
  • 8Deb K. Multi-Objective Optimization using Evolutionary Al- gorithms [M]. UK: Wiley, 2001: 109-112.
  • 9Nebro A J, Durillo J J, Luna F, et al. MOCell: A cellular genetic algorithm for multiobjective optimization [J]. International Journal of Intelligent Systems, 2009, 24 (7): 726-746.
  • 10Knowles J, Corne D. The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization [M]. Proceedings of the 1999 Congress on Evolutionary Com- putation (CEC 99). 1999: 105.

共引文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部