4.4 Article

A Reconstruction and Contact Analysis Method of Three-Dimensional Rough Surface Based on Ellipsoidal Asperity

期刊

出版社

ASME
DOI: 10.1115/1.4045633

关键词

three-dimensional rough surface; sampling interval; asperity; contact analysis; contact; surface properties and characterization; surface roughness and asperities

资金

  1. National Natural Science Foundation of China [51535012, U1604255, 51705142]
  2. Key Research and Development Project of Hunan Province [2016JC2001]
  3. Fundamental Research Funds for the Central Universities of Central South University [2019zzts255]

向作者/读者索取更多资源

The 3D rough surface modeling and contact analysis is a difficult problem in the study of rough surface contact. In this paper, a new method for reconstruction and contact analysis of asperities on 3D rough surfaces is proposed based on real rough surfaces. Watershed algorithm is used to segment and determine the area of asperities on the rough surface. According to the principle of minimum mean square error, ellipsoid fitting is carried out on asperities. Based on the elastic-plastic contact model of a single ellipsoidal asperity, a stable and efficient method for 3D rough surface contact analysis and calculation is proposed. Compared with existing calculating methods, the present method has the following characteristics: (1) the constructed surface asperity is closer to the real asperity in contact, and the calculation of asperity parameters has better stability under different sampling intervals and (2) the contact pressure, contact area, and other contact parameters of the 3D rough surface are calculated with high accuracy and efficiency, and the calculation convergence is desirable. The reconstruction and contact analysis method of the 3D rough surface asperity proposed in this paper provides a more accurate reconstruction and calculation method for the study of contact fatigue life and wear failure of rough surfaces.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据