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Related Experiment Video

Updated: Sep 21, 2025

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Encoding Stability into Laser Powder Bed Fusion Monitoring Using Temporal Features and Pore Density Modelling.

Brian G Booth1, Rob Heylen2, Mohsen Nourazar1

  • 1imec TELIN-IPI, Ghent University, 3000 Leuven, Belgium.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for monitoring laser powder bed fusion (LPBF) stability using temporal features and pore density modeling. This approach enhances printing stability and reduces defects in 3D printed parts.

Keywords:
keyhole poreslack-of-fusion poreslaser powder bed fusionmelt pool monitoringtemporal features

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Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Computational Modeling

Background:

  • Melt pool instability in laser powder bed fusion (LPBF) leads to pores and reduced structural integrity in 3D printed parts.
  • Current camera-based monitoring systems offer limited, indirect insights into melt pool stability.

Purpose of the Study:

  • To improve melt pool stability in LPBF by explicitly encoding stability into monitoring systems.
  • To develop a novel approach using temporal features and pore density modeling for enhanced LPBF process control.

Main Methods:

  • Introduced temporal features (variances of melt pool area, intensity) to quantify printing stability.
  • Developed a neural network model linking video features to pore densities from CT scans.
  • Implemented and tested the monitoring system on 316L stainless steel prints.

Main Results:

  • Achieved improved correlation (up to 42%) between predicted and true pore densities.
  • Demonstrated the effectiveness of explicit stability quantification in LPBF monitoring.
  • Reduced the need for online printer interventions by focusing on porosity avoidance.

Conclusions:

  • Explicitly encoding temporal features and pore density modeling significantly enhances LPBF monitoring.
  • The proposed method offers a more accurate and direct approach to ensuring part quality in 3D printing.
  • This advancement contributes to more reliable and robust additive manufacturing processes.