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Proceedings Paper

Experimental research of high quality grinding technology on SiC based on grinding force
Author(s): Qiancai Wei; Lian Zhou; Xianhua Chen; Shijie Zhao; Nan Zheng; Jie Li; Jian Wang
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Paper Abstract

In order to improve the grinding quality and efficiency of SiC elements, the influence of grinding parameters on the grinding quality was studied by using 2MK1760 ultra-precision surface grinder. Based on the surface roughness and surface precision, the relationship between grinding volume and grinding force of metal bonded and resin bonded diamond grinding wheels was researched, and it was obtained that the metal bonded grinding wheels could maintain enough sharpness during a large amount of grinding removal. The grinding force was used to characterize the grinding wheel wear, and through experiments studied the relationship between grinding parameters (including grinding depth) and the SiC material removal volume after grinding wheel one-time dressing. The results showed that different grinding depth will not affect the grinding wheel life under the same other conditions. On the basis of technological experiments, aiming at optimizing grinding efficiency and quality, resin bonded plane grinding wheel was used to grind SiC elements with three diameters of 75mm, 150mm and 320mm, and very small surface precision (PV=3.758μm) and surface roughness (RMS=35.472nm) could be obtained.

Paper Details

Date Published: 31 January 2020
PDF: 8 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114273M (31 January 2020);
Show Author Affiliations
Qiancai Wei, China Academy of Engineering Physics (China)
Lian Zhou, China Academy of Engineering Physics (China)
Xianhua Chen, China Academy of Engineering Physics (China)
Shijie Zhao, China Academy of Engineering Physics (China)
Nan Zheng, China Academy of Engineering Physics (China)
Jie Li, China Academy of Engineering Physics (China)
Jian Wang, China Academy of Engineering Physics (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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