journal article·2022-11

Effects of incidence angle and optimization in femtosecond laser polishing of C/SiC composites

Chenhu Yuan, Bin Wang, Jiajia Wang, Yunfeng Wang, Liyuan Sheng, Junke Jiao, Jianhua Yao, Yihui Huang, Wenwu Zhang

Ceramics International· Volume 48· Issue 21
Publisher page

Abstract

In this article, C/SiC ceramic matrix composites (CMC) were polished by femtosecond laser. The effects of different laser incidence angles on the machined surface were studied in detail, and the process parameters were optimized. The results have indicated that different laser incident angles influence the machined surface of CMC. With the increase of laser incident angle, the defects and oxidation degree decrease significantly, the carbon (C) content gradually decreases, silica (Si) content gradually increases, and oxygen (O) content is almost unchanged. Under an incident angle of 70°, the surface carbon fiber along the axial direction was neatly cut, and the bump structure was eliminated. The laser's incident angle greatly influences the corrugated structure of the carbon fiber section. The response surface method establishes the quadratic response model of four parameters and surface roughness. The small-scale optimization method predicted the optimal expected value as follows: single pulse energy is 71.53 μJ, defocus amount is −0.13 mm, incident angle is 20.61°, Y-axis spot overlap rate 77.50%, and the optimal surface roughness (Ra) is predicted as 1.03 μm. The experimental results show that the average surface roughness of 0.92 μm is within the 95% confidence interval mean (95% CI Mean), indicating that the experimental results are reliable. It is 51.98% lower than the original surface roughness value of 1.92 μm. The optimal surface roughness value is 0.71 μm, 63.02% lower than the original surface roughness value. © 2022 Elsevier Ltd and Techna Group S.r.l.
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Research context

OpenAlex topics: Laser Material Processing Techniques · Advanced Surface Polishing Techniques · Laser-induced spectroscopy and plasma

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

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