High-Speed Deformation Prediction in Selective Laser Melting Using Context-Adaptive Neural Networks

In selective laser melting (SLM), the 3D-printed metal objects can deform, causing dimensional inaccuracies and potentially damaging the printer's recoater. While layerwise monitoring systems are available to detect these deformations, higher-speed detection would enable a controller to intervene to reduce this defect. In this work we propose the first such in-situ monitoring system that detects part deformations through the analysis of melt pool images. The system uses high-speed cameras with frame rates up to 20,000 frames per second in the visual and short-wave infrared spectrum to record the melt pool. An adaptive weight network is then used to predict deformation magnitudes based on the image data and contextual printing information such as part geometry and hatching patterns. The addition of this contextual information allows the model to normalize for within-layer printing variations when interpreting the image data. Our model was validated on three print ob-jects that display deformations resulting from different physical sources. Our model's predictions were compared to deformation measurements taken from X-ray CT scans and showed both a strong correlation with the ground truth, with average relative errors between 17 % and 22%, and generalized well to untrained deformation types. The prediction model is also able to operate at very high speeds (> 20 kHz) to facilitate high-speed intra-layer control loops.