  In-situ monitoring system can be used to monitor the quality of additive
manufacturing (AM) processes. In the case of digital image correlation (DIC)
based in-situ monitoring systems, high-speed cameras were used to capture
images of high resolutions. This paper proposed a novel in-situ monitoring
system to accelerate the process of digital images using artificial
intelligence (AI) edge computing board. It built a visual transformer based
video super resolution (ViTSR) network to reconstruct high resolution (HR)
videos frames. Fully convolutional network (FCN) was used to simultaneously
extract the geometric characteristics of molten pool and plasma arc during the
AM processes. Compared with 6 state-of-the-art super resolution methods, ViTSR
ranks first in terms of peak signal to noise ratio (PSNR). The PSNR of ViTSR
for 4x super resolution reached 38.16 dB on test data with input size of 75
pixels x 75 pixels. Inference time of ViTSR and FCN was optimized to 50.97 ms
and 67.86 ms on AI edge board after operator fusion and model pruning. The
total inference time of the proposed system was 118.83 ms, which meets the
requirement of real-time quality monitoring with low cost in-situ monitoring
equipment during AM processes. The proposed system achieved an accuracy of
96.34% on the multi-objects extraction task and can be applied to different AM
processes.
