Fracture Prediction in Brittle Materials

Abstract

The use of ceramics in scientific and industrial applications is limited by their relatively poor mechanical properties, brittleness and variability in strength. Design with brittle materials requires an entirely new concept from that with ductile materials. One must think in terms of probability rather than virtual certainty. Statistical flaw theory based on the weakest-link hypothesis has been applied to problems of fracture and failure of brittle materials under test loads. One such theory, the Weibull distribution, which is probably the most widely applied, addresses the statistical variation of strength and the size effect due to flaws, because in a variety of conditions both its mathematical formulation and the estimation of the Weibull parameters from experiments are simple. Many techniques exist to determine the Weibull parameters from the series of data obtained from experimental tests. The Weibull distribution expressions have been developed for the pure bending and torsion stress states for the fracture of hollow alumina tubes. Such tests are relatively inexpensive and provide useful data reliably but no closed-form solution exists for the probability formulations. An iteration method based on the least-square minimization of residual errors in the test results is used to determine the Weibull parameters.

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Document Details

Document Type
Technical Report
Publication Date
May 29, 1981
Accession Number
ADA112452

Entities

People

  • George J. Filatovs
  • William J. Craft

Organizations

  • North Carolina Agricultural and Technical State University

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aluminum Oxides
  • Birds
  • Computer Programs
  • Crystal Structure
  • Experimental Data
  • Goodness Of Fit Tests
  • Materials
  • Materials Laboratories
  • Materials Science
  • Mechanical Properties
  • Mechanics
  • Military Research
  • Probability Distributions
  • Reliability
  • Statistical Algorithms
  • Structural Components
  • Tensile Strength

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation
  • Materials Science (Mechanical Engineering).