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Journal of Materials Science

, Volume 29, Issue 7, pp 1739–1752 | Cite as

Effect of glass-fibre reinforcement and annealing on microstructure and mechanical behaviour of nylon 6,6

Part II Mechanical behaviour
  • M. L. Shiao
  • S. V. Nair
  • P. D. Garrett
  • R. E. Pollard
Papers

Abstract

The effects of glass-fibre reinforcement and annealing on the deformation and fracture behaviour of nylon 6,6 were investigated. The roles of glass fibres were examined by varying the glass fibre content and the fibre length, and by in situ fracture studies in front of crack tips. The effects of microstructural changes were investigated by imposing various annealing conditions on the specimens. The results indicated that the fracture toughness showed a sharp decrease due to stress concentrations at fibre ends when the fibre volume fraction was small. Above a critical fibre volume fraction, it was found that the fracture toughness can be substantially increased by enhanced localized matrix plasticity at fibre ends. The competing roles of glass fibre ends were consistent with microstructure sensitive fracture mechanics models of failure based on the attainment of a critical stress or strain over a critical microstructural distance in the crack-tip region. Upon annealing above a critical annealing time the unreinforced nylon 6,6 showed a drastic decrease in the strength and ductility, corresponding to a loss of the constant-load deformation region prior to necking. However, the fracture toughness of unreinforced nylon 6,6 was only moderately reduced by annealing. On the other hand, the fracture toughness of the composites showed a significant increase upon annealing. The combined effects of glass fibres and annealing on microstructures and overall property optimization of the composites are also discussed.

Keywords

Fracture Toughness Glass Fibre Fibre Length Fibre Volume Fraction Localize Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Chapman & Hall 1994

Authors and Affiliations

  • M. L. Shiao
    • 1
  • S. V. Nair
    • 1
  • P. D. Garrett
    • 2
  • R. E. Pollard
    • 2
  1. 1.Department of Mechanical EngineeringUniversity of MassachusettsAmherstUSA
  2. 2.Monsanto Chemical Co.SpringfieldUSA

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