Uncertainty quantification of grain morphology in laser direct metal deposition

Paromita Nath, Zhen Hu, Sankaran Mahadevan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Uncertainty quantification (UQ) has an important role to play in the quality control of additively manufactured products. With a focus on laser direct metal deposition (LDMD), this work presents a systematic UQ framework to quantify the uncertainty of grain morphology due to various sources of uncertainty in the LDMD simulation process. The LDMD process is simulated by a coupled analysis consisting of a macroscale finite element model for the melt pool and a microscale cellular automata model for solidification to predict the microstructure. UQ is carried out using singular value decomposition-based Kriging surrogate of the expensive simulation model. The effectiveness of the proposed approach is demonstrated by identifying several major sources of uncertainty and studying their contributions to the uncertainty in the grain size distribution using a variance-based sensitivity analysis method.

Original languageEnglish (US)
Article number044003
JournalModelling and Simulation in Materials Science and Engineering
Issue number4
StatePublished - Apr 24 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Computer Science Applications


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