journal article·2024-09

Improving zirconia ceramics grinding surface integrity through innovative laser bionic surface texturing

Xiaohong Zhang, Tianzhongsen He, Dongdong Wen, Tielin Li, Xun Chen, Chao Li, Yuejiao Ding, Xiong Tang, Zhiyuan Yang

Ceramics International· Volume 50· Issue 18
Publisher page

Abstract

Zirconia ceramics have become an important material for the manufacture of key components in aerospace, military, energy, and other fields due to their excellent mechanical properties. However, their inherent high hardness and brittleness make them difficult to machine with high efficiency and low damage, resulting in poor surface integrity. Inspired by the ribbed groove structure of shark skin and the fractal structure of insect wing veins, two zirconia ceramics with different bionic textures are designed in this paper, and zirconia ceramic surfaces are bionically textured by using an ultrafast picosecond pulse laser. The effects of the special bionic textures on the grinding behavior of zirconia ceramics, specifically surface morphology, surface roughness, edge morphology, and subsurface damage, were investigated. The experimental results show that the surface integrity of the bionic textured zirconia ceramics is better compared to the untextured surface. In particular, the bionic insect wing veins texture exhibits the best surface quality with the least sub-surface damage, and the surface roughness Sa is reduced by a maximum of 21.46 %, and Sz is reduced by a maximum of 18.87 %. However, the best edge morphology is exhibited by the bionic shark skin texture. Additionally, bionic texturing improves grinding lubrication and cooling conditions. The results confirm that applying bionic texturing to zirconia ceramics effectively enhances grinding performance and improves grinding surface integrity.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Laser Material Processing Techniques · Advanced Surface Polishing Techniques · Surface Roughness and Optical Measurements

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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