• Corpus ID: 218900607

Discovering universal scaling laws in 3D printing of metals with genetic programming and dimensional analysis

@article{Gan2020DiscoveringUS,
  title={Discovering universal scaling laws in 3D printing of metals with genetic programming and dimensional analysis},
  author={Zhengtao Gan and Orion L. Kafka and Niranjan D. Parab and Cang Zhao and Olle G. Heinonen and Tao Sun and Wing Kam Liu},
  journal={arXiv: Applied Physics},
  year={2020}
}
We leverage dimensional analysis and genetic programming (a type of machine learning) to discover two strikingly simple but universal scaling laws, which remain accurate for different materials, processing conditions, and machines in metal three-dimensional (3D) printing. The first one is extracted from high-fidelity high-speed synchrotron X-ray imaging, and defines a new dimensionless number, Keyhole number, to predict melt-pool vapor depression depth. The second predicts porosity using the… 
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