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An Image-Based Transfer Learning Approach for Using In Situ Processing Data to Predict Laser Powder Bed Fusion

Qixiang Luo1, John D Shimanek1, Timothy W Simpson2,3

  • 1Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania, USA.

3D Printing and Additive Manufacturing
|March 28, 2025
PubMed
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This summary is machine-generated.

Machine learning accurately predicts mechanical properties of 3D-printed Ti-6Al-4V using laser powder bed fusion (PBF-LB) signals. This approach enables real-time process optimization for enhanced additive manufacturing reliability.

Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computer Science

Background:

  • Additive manufacturing (AM) requires defect mitigation for reliable parts.
  • Modeling build monitoring signals and mechanical performance is key.
  • Ti-6Al-4V is a critical alloy in AM applications.

Purpose of the Study:

  • Investigate a machine learning approach to predict mechanical properties of Ti-6Al-4V parts fabricated via PBF-LB.
  • Utilize in situ photodiode processing signals for property prediction.
  • Explore transfer learning for enhanced prediction accuracy.

Main Methods:

  • Fabricated Ti-6Al-4V samples using PBF-LB with varied laser powers and scan speeds.
  • Collected in situ photodiode data during fabrication.
Keywords:
computer visiondeep convolution neural networkin situ processing monitoringlaser powder bed fusionmachine learning

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  • Applied deep convolutional neural networks (DCNNs) for feature extraction from photodiode signals.
  • Trained a regression model using extracted features to predict ultimate tensile strength and elongation to fracture.

    • Main Results:

    • Achieved 98.7% accuracy (r²=0.89) in predicting ultimate tensile strength (900-1150 MPa).
    • Achieved 93.1% accuracy (r²=0.96) in predicting elongation to fracture (0-17%).
    • Demonstrated high accuracy and hardware-accelerated inference speeds.

    Conclusions:

    • A transfer learning framework effectively predicts strength and ductility of metal AM components using PBF-LB processing signals.
    • This method offers a potential route for real-time closed-loop control in PBF-LB.
    • Highlights a pathway for process optimization in industrial additive manufacturing applications.