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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.
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.
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.
- 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.
