Apply Deep Learning to Powder Bed Fusion Process Physics in 3D Printing for Smart Calibration and Control
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Although deep learning has been successfully implemented in processing images and video data, it still has limited applications in investigating physical processes for smart process calibration and control. Collaborating with HP research scientists, this study demonstrates how engineering-informed deep learning can assist to understand the powder bed fusion in 3D printing processes. The studies show promising model performance in both prediction quality (e.g., accuracy) and the computational cost.
Submitted by Qiang Huang
on
pdf
Although deep learning has been successfully implemented in processing images and video data, it still has limited applications in investigating physical processes for smart process calibration and control. Collaborating with HP research scientists, this study demonstrates how engineering-informed deep learning can assist to understand the powder bed fusion in 3D printing processes. The studies show promising model performance in both prediction quality (e.g., accuracy) and the computational cost.
Submitted by Qiang Huang
on