Calibrating uncertain parameters in melt pool simulations of additive manufacturing

Knapp, Gerry L.; Coleman, John; Rolchigo, Matt; Stoyanov, Miroslav; Plotkowski, Alex

doi:10.1016/j.commatsci.2022.111904

Calibrating uncertain parameters in melt pool simulations of additive manufacturing

Journal Article · Wed Nov 23 00:00:00 EST 2022 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2022.111904· OSTI ID:1901636

^[1]; Coleman, John ^[1]; Rolchigo, Matt ^[1]; ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Melt pool scale numerical modeling of additive manufacturing (AM) processes can provide predictive capabilities and theoretical insight into the process-property-structure-performance relationships for AM parts. Despite capabilities of numerical models to solve complex multi-physics problems, it is often important to consider a tradeoff between detailed physics and computational cost. Therefore, sources of uncertainty in both experimental conditions and the parameters needed for modeling require models to be validated against empirical evidence. Here, a method is proposed to calibrate uncertain parameters used in continuum-scale melt pool models for powder bed fusion (PBF) AM. Both a simplified heat transfer model and a heat transfer and fluid flow model were investigated. A surrogate model and Markov chain-based optimization algorithm calibrated melt pool geometry for models within experimental variation of the target melt pool width and depth from the NIST AM-Bench 2018-02 dataset. The melt pool temperature distributions, solidification parameters, and simulated multi-layer solidification microstructures were compared between the two models. Similar results from both models indicate that calibrated, lower fidelity numerical models may be used in place of higher fidelity models to generate melt pool solidification data. Finally, these calibrated models therefore enable lower computational cost melt pool simulations without a noticeable decrease in simulation accuracy for grain-scale microstructure simulations.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1901636

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 218; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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