Hybrid Data Fusion for Low-Cost High-Speed Monitoring in Powder Bed Fusion 3D Printing

In laser powder bed fusion 3D printing, defects can be created in less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100\ \mu\mathrm{s}$</tex>, making it difficult and expensive to monitor and control the process in real time. Existing techniques either rely on the use of simple, high-speed sensors for control purposes (e.g., photodiodes) or high-resolution, lowspeed sensors (e.g., cameras) to analyze print defects. Since both techniques have limitations (either lack of accuracy or speed in defect detection), we propose that fusing both sensor types can provide detailed print quality indicators at a high enough speed for real-time control. Our data fusion works in a hybrid fashion by first processing images from a high-speed camera (5 kHz) to compute detailed print quality indicators (melt pool size & intensity, number of spatters). These print quality indicators are then fused with very high speed photodiode values (100 kHz) to extrapolate the print quality indicators to 100 kHz. An adaptive weight network is used to perform the fusion and extrapolation, allowing the low-speed print quality indicators to provide context for the interpretation of the photodiode values. Our results on three print data sets show that the proposed data fusion produces print quality indicators within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\ \mu\mathrm{s}$</tex> that are significantly more accurate than using only one sensor type alone. The speed, accuracy, and relatively low cost of the proposed data fusion make it a credible option for low-cost monitoring and control of such high-speed processes.