| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Presentation Title |
Pre-training Vision Encoders with Thermal Images for In-situ Process Monitoring in Laser Powder Bed Fusion |
| Author(s) |
Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky, Anthony Garland, Dan S. Bolintineanu, Dan R. Moser, Brad Salzbrenner, Mary Arnhart, Jonathan Madison, Thomas Ivanoff, John Mitchell, Bradley Jared, Michael J. Heiden, Amir Barati Farimani |
| On-Site Speaker (Planned) |
Peter Myung-Won Pak |
| Abstract Scope |
We investigate the use of melt pool thermal image datasets to pre-train vision encoders for real-time process monitoring in additive manufacturing. Feature learning is achieved through performing tasks such as inpainting temperature fields within masked melt pool regions. Different encoder mechanisms such as Vision Transformers or Convolutional Neural Networks are evaluated and compared against one another. By training our models on publicly available data from NIST and validating the models with experimental data, we enhance its prediction accuracy and enable its use in downstream tasks within in-situ monitoring systems. The features learned from this data can be correlated to those from optical imaging, acoustic signaling, or computed tomography. With this work we aim to improve quality control and process optimization in additive manufacturing environments. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |