Fracture Toughness Improvement of Composites Reinforced with Optimally Shaped Short Ductile Fibers

Abstract

The fracture toughness of brittle matrix composites reinforced with ductile fibers has been greatly improved by shaping the fibers so that they fully contribute their plastic work to the fracture process. This has been accomplished by anchoring the fiber ends so that the largest possible fiber volume contributes to the toughness during pullout. A combined experimental and analytical program has shown that it is possible to organize fibers into shape families and to optimize the toughness with respect to geometric parameters within a given shape family. The interface conditions during pullout have been modeled using a new cohesive zone/Coulomb friction model with three parameters. Furthermore, adaptive methods in space and time were developed to reduce computational time. New indicators for error in spatial and temporal discretizations were derived and implemented. Once these parameters were inferred from one embedded length from the single fiber pullout test (SFP), predicted pullout toughness values at other embedded depths matched well with experimental results. In addition, fracture values predicted from SFP tests matched well with experimental composite fracture values. A list of 19 publications and technical reports on this topic and a bibliography are included.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2001
Accession Number
ADA429880

Entities

People

  • Abani K. Patra
  • Robert C. Wetherhold

Organizations

  • State University of New York at Albany

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Adhesion
  • Applied Mechanics
  • Composite Materials
  • Epoxy Composites
  • Fiber Reinforced Composites
  • Fibers
  • Friction
  • Materials
  • Materials Science
  • Mechanical Properties
  • Mechanics
  • Metal Fibers
  • Shear Stresses
  • Shielding
  • Simulations
  • Thermosetting Plastics
  • Toughness

Fields of Study

  • Materials science

Readers

  • Computational Modeling and Simulation
  • Materials Science (Mechanical Engineering).
  • Reinforced Composite Materials

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space