Scholarlink

<p>Sliding friction of nanocrystalline silicon nitride under extreme conditions inevitably causes subsurface damage and degradation of structural performance. To investigate the subsurface damage mechanism under dry friction, a molecular dynamics model of a fractal rough surface is constructed using the Weierstrass–Mandelbrot fractal function. A deep learning interatomic potential for silicon nitride is applied to accurately describe atomic interactions. The sliding process is divided into loading and friction stages by defining a sliding path. Subsurface volume damage evolution and microcrack behavior under dry friction are systematically analyzed. A significant coupling relationship is found between the variation of the friction coefficient and the subsurface volumetric wear rate. The coupling degree is regulated by the friction depth. The fracture and evolution of microcracks are revealed through bond breaking and volumetric strain analysis. Crack initiation and growth are strongly promoted in the 0- to 0.01-nm depth range during the loading stage. In addition, the fractal surface morphology is identified as a key factor influencing the distribution of vacancy defects and the direction of microcrack propagation. This study provides theoretical support and methodological guidance for the wear-resistant design and reliability prediction of precision ceramic materials.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sliding Friction Subsurface Wear on Fractal Rough Surfaces of Nano silicon nitride under Dry Friction Conditions Molecular Dynamics Analysis

  • 期刊论文

  • 发表日期：

    2025年6月23日

查看全文
  • Jianbo Le,
  • Juan Liu,
  • Jinyu Chen,
  • Hong Jiang,
  • Yi Jiang,
  • Nanxing Wu

摘要

Sliding friction of nanocrystalline silicon nitride under extreme conditions inevitably causes subsurface damage and degradation of structural performance. To investigate the subsurface damage mechanism under dry friction, a molecular dynamics model of a fractal rough surface is constructed using the Weierstrass–Mandelbrot fractal function. A deep learning interatomic potential for silicon nitride is applied to accurately describe atomic interactions. The sliding process is divided into loading and friction stages by defining a sliding path. Subsurface volume damage evolution and microcrack behavior under dry friction are systematically analyzed. A significant coupling relationship is found between the variation of the friction coefficient and the subsurface volumetric wear rate. The coupling degree is regulated by the friction depth. The fracture and evolution of microcracks are revealed through bond breaking and volumetric strain analysis. Crack initiation and growth are strongly promoted in the 0- to 0.01-nm depth range during the loading stage. In addition, the fractal surface morphology is identified as a key factor influencing the distribution of vacancy defects and the direction of microcrack propagation. This study provides theoretical support and methodological guidance for the wear-resistant design and reliability prediction of precision ceramic materials.