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Multiple Sensor Detection of Process Phenomena in Laser Powder Bed Fusion.

Brandon Lane1, Eric Whitenton1, Shawn Moylan1

  • 1National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899, USA.

Proceedings of Spie--The International Society for Optical Engineering
|March 14, 2020
PubMed
Summary

This study integrates high-speed imaging and single-point sensors for laser powder bed fusion (LPBF) monitoring. Synchronized data analysis reveals melt-pool dynamics, aiding in defect mitigation for additive manufacturing.

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Area of Science:

  • Materials Science and Engineering
  • Additive Manufacturing
  • Process Monitoring

Background:

  • Laser powder bed fusion (LPBF) parts often suffer from residual stresses, anisotropic microstructure, and defects.
  • In-situ monitoring of melt-pool phenomena is crucial for understanding and improving part quality in LPBF.
  • High data acquisition rates (exceeding 10 kHz) are needed to capture rapid melt-pool dynamics, challenging real-time monitoring systems.

Purpose of the Study:

  • To investigate the relationship between melt-pool phenomena and part quality in LPBF.
  • To explore the use of synchronized, multi-sensor data acquisition for in-situ process monitoring.
  • To develop signal processing techniques for identifying critical process signatures.

Main Methods:

  • Synchronized in-situ data acquisition using a thermal camera, high-speed visible camera, photodiode, and laser modulation signal on a commercial LPBF machine.
Keywords:
additive manufacturinglaser powder bed fusionphotodetectorthermography

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  • Fabrication of a nickel alloy 625 part with an overhang geometry.
  • Joint-time frequency analysis (JTFA) of the photodiode signal to identify process signatures.
  • Image fusion of synchronized data from multiple build layers for visual correlation.

    • Main Results:

    • Thermal camera data provided temperature information, while the visible camera captured spatter.
    • The photodiode signal offered high temporal bandwidth brightness data from the melt pool.
    • JTFA successfully highlighted specific signatures within the photodiode signal, indicating required filtering and processing.
    • Image fusion enabled visual comparison between photodiode signals and observed phenomena.

    Conclusions:

    • Synchronized multi-sensor data acquisition is effective for in-situ monitoring of LPBF processes.
    • Combining imaging and single-point sensor data, along with advanced signal processing like JTFA, enhances understanding of melt-pool dynamics.
    • This approach provides a pathway for real-time feedback control to mitigate defects and improve the quality of additively manufactured parts.