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Prediction of Elastic Modulus of Glass Short Fiber/Wood Powder/Polypropylene Hybrid Composites

[+] Author Affiliations
Ying Yu, Hiroyuki Hamada

Kyoto Institute of Technology, Kyoto, Japan

Manabu Nomura

Green Plaisir, Osaka, Japan

Paper No. IMECE2013-66679, pp. V009T10A029; 6 pages
  • ASME 2013 International Mechanical Engineering Congress and Exposition
  • Volume 9: Mechanics of Solids, Structures and Fluids
  • San Diego, California, USA, November 15–21, 2013
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5638-3
  • Copyright © 2013 by ASME


Recent years, thermoplastics incorporated with particulate fillers have been gained high interests. To improve the mechanical properties of the natural particle reinforced polymer plastics, hybrid structure has been applied on the composite combining natural particle with stronger synthetic fibers. However, the reinforcing mechanism of the hybrid composite is quite complicated. Experiments on it may become time consuming and cost prohibitive. Therefore, researchers are interested in studying variable models to predict the elastic properties of the composites.

In this study, glass short fiber/wood particle/pp hybrid composites were prepared by injection molding process at a fixed reinforcement to matrix ratio of 51:49. 4 kinds of hybrid specimens with glass fiber/wood particle ratios of 41:10, 31:20, 21:30 and 11:40 were fabricated. The effect of hybridization content on the mechanical properties of the composites was evaluated based on tensile test. Theoretically, the elastic modulus of hybrid composites was predicted by using the rule of hybrid mixtures (RoHM) equation and classical lamination theory (CLT) and the accuracy of the two estimation models has been discussed.

Results showed that it can be considered the hybridization of wood powder into glass/PP composite could contribute to a similar high elastic modulus with high green degree. On the other hand, the fiber orientation factor, fiber length distribution factor, powder dispersion factor were very important factors and need to be considered in the prediction model.

Copyright © 2013 by ASME



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