Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process
<|reference_start|>Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process: Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisions. Data-driven methods from reduced order modeling and system identification are particularly promising in managing this data deluge. This study focuses on Laser Powder Directed Energy Deposition (LP-DED) equipped with in-situ process measurements to develop a compact virtual test-bed. This test-bed can accurately ingest arbitrary process inputs and report in-process observables as outputs. This virtual test-bed is derived using Dynamic Mode Decomposition with Control (DMDc) and is coupled with uncertainty quantification techniques to ensure robust predictions.<|reference_end|>
@article{juhasz2024data-driven,
    title={Data-driven Virtual Test-bed of the Blown Powder Directed Energy
  Deposition Process},
    author={Michael Juhasz, Eric Chin, Youngsoo Choi, Joseph T. McKeown, Saad
  Khairallah},
    journal={arXiv preprint arXiv:2409.09092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09092},
    primaryClass={eess.SY cond-mat.mtrl-sci cs.SY}
}