LEGOMAT: Locally Extracted Globally Organized Microstructure Models using Markov Random Fields

Abstract

The problem of microstructure reconstruction is deeply intertwined with the theories of microstructure quantification. Currently available methods for microstructure synthesis such as geometry based (Voronoi models), physically based (Phase field models) or feature-based (Simulated annealing) methods run into various difficulties when modeling real complexities of microstructures that include non-equilibrium features, non-convex grains, twins, second phases and cell structures that arise from thermomechanical processing. These features play an important role in the properties and performance of modern aerospace alloys. Further, microstructures are stochastic and lead to location-specific variability in material properties. In this proposal, we delve into a mathematical model that is expected to provide a better alternative for microstructure synthesis: Markov random fields. In the report, theory and software for building 3D microstructural maps of engineering components through inference from 2D measurements is proposed.

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Document Details

Document Type
Technical Report
Publication Date
Jun 27, 2021
Accession Number
AD1144431

Entities

People

  • Veera Sundararaghavan

Organizations

  • Board of Regents of the University of Michigan

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Additive Manufacturing
  • Computational Fluid Dynamics
  • Computational Science
  • Computer-Aided Design
  • Construction
  • Crystal Structure
  • Data Mining
  • Data Science
  • Geometric Forms
  • Information Science
  • Integrated Computational Materials Engineering
  • Lines (Geometry)
  • Manufacturing
  • Materials
  • Materials Engineering
  • Materials Processing
  • Materials Science
  • Materials Testing
  • Mechanics
  • Supervised Machine Learning
  • Three Dimensional
  • Two Dimensional

Readers

  • Computational Fluid Dynamics (CFD)
  • Computational Modeling and Simulation
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space