Application of Adaptive Learning Network Modeling to Single-Particle Erosion Test Data

Abstract

The methodology of Adaptive Learning Network (ALN) Training has been used to model mass loss ratios resulting from single-particle impacts on carbon- carbon composite materials. The resulting ALN models identify material bulk density, graphitization temperature, dynamic hardness (A), and the stiffness exponent (N) as the parameters that most affect the erosion resistance of Series 300 and Series 400 materials. The development of more detailed models was made difficult by the limited variety in the experimental data base. Keywords: Erosion resistance; Carbon-carbon composites.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA221857

Entities

People

  • Francis J. Cook
  • Joseph N. Craig

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Carbon Carbon Composites
  • Chemical Vapor Deposition
  • Composite Materials
  • Databases
  • Erosion Resistance
  • Experimental Data
  • Fibers
  • Graphitic Materials
  • Hardness
  • Impact Tests
  • Literature Surveys
  • Materials
  • Materials Laboratories
  • Materials Science
  • Materials Testing
  • Physical Properties
  • Resistance

Readers

  • Artificial Intelligence
  • Powder metallurgy of Titanium alloys.
  • Reinforced Composite Materials