Jongchan Pyeon’s research while affiliated with Carnegie Mellon University and other places

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Publications (1)


Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics
  • Article

November 2021

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91 Reads

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7 Citations

Integrating Materials and Manufacturing Innovation

Jongchan Pyeon

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Runbo Jiang

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Anthony D. Rollett

During laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al-4V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

Citations (1)


... More specifically, sufficient data for machine learning-based pore prediction for many types of LPBF systems, conditions, and alloys, will require very large efforts to label keyhole morphologies unless accessible automatic tools that can segment the keyholes are developed. Several automatic tools have recently been explored in previous works: Pyeon et al. developed a non-machine learning-based semi-automatic keyhole region extraction tool [13], and Zhang et al. tested several semantic segmentation and object detection models and compared the performances of extracting keyholes and pores at the same time [14]. However, the filter developed by Pyeon et al. was only tested with clean images without metal powder, and models tested by Zhang et al. segment both keyholes and pores in the same classification. ...

Reference:

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion
Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics
  • Citing Article
  • November 2021

Integrating Materials and Manufacturing Innovation