In-situ monitoring additive manufacturing process with AI edge computing

Abstract

In additive manufacturing (AM), in-situ monitoring systems are vital for ensuring process quality. However, the widespread adoption of traditional high-speed camera-based monitoring systems is hindered by the prohibitively high cost of the required high-speed cameras. This paper introduces an innovative low-cost in-situ monitoring system that utilizes AI edge computing boards to expedite digital image processing without requiring high resolution (HR) video sequences. The system integrates a visual transformer-based video super resolution (ViTSR) network for reconstructing high resolution video frames and employs a fully convolutional network (FCN) to extract geometric characteristics of the molten pool and plasma arc simultaneously during AM processes. Comparing ViTSR with six state-of-the-art super-resolution methods, it achieved the highest peak signal-to-noise ratio (PSNR) of 38.16 dB on the test data. Moreover, the FCN utilized the reconstruction results from ViTSR, demonstrating an accuracy of 96.34% in multi-object extraction tasks. Through operator fusion and model pruning, the inference time of ViTSR and FCN on the AI edge board is optimized to 50.97 ms and 67.86 ms, respectively. Consequently, the proposed system achieves a total inference time of 118.83 ms, meeting the real-time quality monitoring needs of AM processes. Furthermore, this approach effectively reduces the expenses associated with high-speed camera-based monitoring systems, thereby promoting the widespread adoption of in-situ monitoring systems within the realm of AM.