Critical Void Content for Polymer Composite Laminates

AIAA Journal ◽  
2005 ◽  
Vol 43 (6) ◽  
pp. 1336-1341 ◽  
Author(s):  
Michelle Leali Costa ◽  
Sergio Frascino Muller de Almeida ◽  
Mirabel Cerqueira Rezende
2006 ◽  
Vol 43 (17) ◽  
pp. 1775-1790 ◽  
Author(s):  
Zhan-Sheng Guo ◽  
Ling Liu ◽  
Bo-Ming Zhang ◽  
Shanyi Du

2019 ◽  
Author(s):  
Kristian Gjerrestad Andersen ◽  
Gbanaibolou Jombo ◽  
Sikiru Oluwarotimi Ismail ◽  
Segun Adeyemi ◽  
Rajini N ◽  
...  

2018 ◽  
Vol 215 ◽  
pp. 31-34 ◽  
Author(s):  
N.Z. Borba ◽  
L. Blaga ◽  
J.F. dos Santos ◽  
S.T. Amancio-Filho

Author(s):  
M. Akif Yalcinkaya ◽  
Gorkem E. Guloglu ◽  
Maya Pishvar ◽  
Mehrad Amirkhosravi ◽  
E. Murat Sozer ◽  
...  

Vacuum-assisted resin transfer molding (VARTM) has several inherent shortcomings such as long mold filling times, low fiber volume fraction, and high void content in fabricated laminates. These problems in VARTM mainly arise from the limited compaction of the laminate and low resin pressure. Pressurized infusion (PI) molding introduced in this paper overcomes these disadvantages by (i) applying high compaction pressure on the laminate by an external pressure chamber placed on the mold and (ii) increasing the resin pressure by pressurizing the inlet resin reservoir. The effectiveness of PI molding was verified by fabricating composite laminates at various levels of chamber and inlet pressures and investigating the effect of these parameters on the fill time, fiber volume fraction, and void content. Furthermore, spatial distribution of voids was characterized by employing a unique method, which uses a flatbed scanner to capture the high-resolution planar scan of the fabricated laminates. The results revealed that PI molding reduced fill time by 45%, increased fiber volume fraction by 16%, reduced void content by 98%, improved short beam shear (SBS) strength by 14%, and yielded uniform spatial distribution of voids compared to those obtained by conventional VARTM.


2018 ◽  
Vol 40 (8) ◽  
pp. 3122-3130 ◽  
Author(s):  
Xiaobo Yang ◽  
Lihua Zhan ◽  
Chengbiao Jiang ◽  
Xing Zhao ◽  
Chenglong Guan

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