Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure Through Advanced Homogenization

Sourav Saha, Orion L. Kafka, Ye Lu, Cheng Yu, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model, solved within the fast Fourier transformation (FFT) framework for mechanics, to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called self-consistent clustering analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.

Original languageEnglish (US)
Pages (from-to)360-372
Number of pages13
JournalIntegrating Materials and Manufacturing Innovation
Volume10
Issue number3
DOIs
StatePublished - Sep 2021

Keywords

  • Additive manufacturing
  • Homogenization
  • IN625
  • Proper generalized decomposition
  • Self-consistent clustering analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Industrial and Manufacturing Engineering

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