Ceramic Life Prediction Parameters

Abstract

This program consisted of a basic study using two potential high temperature ceramic materials, hot-pressed silicon nitride, NC-132 (NORTON), and hot-pressed silicon nitride made with 3.5 wt. percent MgO (Ford material) to establish a statistical strength data base for fast fracture as well as for the presence of subcritical crack growth. The Weibull characteristic strength and modulus were determined. Among the fracture mechanics approach, the primary experimental techniques were double-torsion and indentation-induced flaw methods to determine the relationship between crack velocity, V, and stress intensity, K, during subcritical crack growth for NC-132 Si3N4. The subcritical crack growth exponent 'n' was determined using flexural stress and strain rate methods and stress rupture methods, and showed a wide scatter in magnitude. When all the relevant life prediction parameters such as inherent flaw size, strength, critical stress intensity factor, and K-V relationship for slow crack growth are known, as estimate of time-to-failure for a given applied stress, temperature and environment can be made using the numerical relationships outlined by Evans and Wiederhorn earlier. Care should be taken in selecting the appropriate parameters since these parameters are a function of evaluation technique, otherwise the predicted time-to-failure will show a large variation.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1980
Accession Number
ADA090272

Entities

People

  • R. K. Govila

Organizations

  • Ford Motor Company

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms

DTIC Thesaurus Topics

  • Aluminum Oxides
  • Ceramic Materials
  • Creep
  • Databases
  • Fast Fractures
  • Fracture (Mechanics)
  • Fungi
  • High Temperature
  • Materials
  • Mechanical Properties
  • Mechanical Working
  • Mechanics
  • Strain Rate
  • Stress Intensity Factors
  • Tensile Stress
  • Test And Evaluation
  • Turbines

Readers

  • Materials Science (Mechanical Engineering).
  • Regression Analysis.