Microstructure-Sensitive Notch Root Analysis for Ni-Base Superalloys (Preprint)

Abstract

Macroscopic viscoplastic constitutive models for y-y Ni-base superalloys typically do not contain an explicit dependence on the underlying microstructure. Microstructure dependent models are of interest since the sizes, volume fractions, and morphologies of primary, secondary, and tertiary precipitates can substantially affect the stress-strain response. The principle microstructural features that can significantly affect the stress-strain response of y-y Ni-base superalloys are the grain size and precipitate volume fraction and size distributions. An Artificial Neural Network (ANN) is used to correlate the material parameters in an internal state variable cyclic viscoplasticity model with these microstructure plasticity calculations performed on other microstructures within the range characterized experimentally. The trained model is applied to an example of component notch root analyses to explore the potential impact of microstructure-sensitive constitutive models in fatigue design of structures.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2007
Accession Number
ADA470002

Entities

People

  • Craig Przybyla
  • David Mcdowell
  • Mahesh Shenoy
  • Yustianto Tjiptowidjojo

Organizations

  • Georgia Tech

Tags

DTIC Thesaurus Topics

  • Advanced Materials
  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Chemical Compounds
  • Department Of Defense
  • Engineering
  • Governments
  • Grain Size
  • Materials
  • Materials Engineering
  • Materials Science
  • Microstructure
  • Military Research
  • Superalloys
  • Viscoplasticity

Fields of Study

  • Materials science

Readers

  • Mechanical Engineering/Mechanics of Materials.
  • Neural Network Machine Learning.
  • Powder metallurgy of Titanium alloys.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks