Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling

Zhuo Wang, Pengwei Liu, Yanzhou Ji, Sankaran Mahadevan, Mark F. Horstemeyer, Zhen Hu, Lei Chen, Long Qing Chen

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The complicated metal-based additive manufacturing (AM) process involves various sources of uncertainty, leading to variability in AM products. For comprehensive uncertainty quantification (UQ) of AM processes, we present a physics-informed data-driven modeling framework, in which multilevel data-driven surrogate models are constructed based on extensive computational data obtained by multiscale multiphysics AM models. It starts with computationally inexpensive surrogate models for which the uncertainty can be readily quantified, followed by global sensitivity analysis for comprehensive UQ study. Using AM-fabricated Ti-6Al-4V components as examples, this study demonstrates the capability of the proposed data-driven UQ framework for efficient investigation of uncertainty propagation from process parameters to material microstructures, then to macrolevel mechanical properties through a combination of advanced AM multiphysics simulations and data-driven surrogate modeling. Model correction and parameter calibration for the constructed surrogate models using limited amounts of experimental data are discussed.

Original languageEnglish (US)
Pages (from-to)2625-2634
Number of pages10
JournalJOM
Volume71
Issue number8
DOIs
StatePublished - Aug 15 2019

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Engineering(all)

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