Wuhan University

Shengnan Shen

Hui Li

Yu Zhang

Wenkang Zhu

Xiuhua Li

Zhixin Bao

A real-time edge computing method and system for additive manufacturing monitoring are presented. The method involves creating a dataset, developing an online monitoring model, and training it with the dataset. The trained model is then deployed on an edge device to monitor the additive manufacturing process in real-time. The system comprises an edge device and a high-speed camera for capturing and transmitting manufacturing footage to the edge device, which analyzes the data using the deployed model for continuous process monitoring.

Edge computing-based additive manufacturing real-time monitoring method and system

A method and system for 3D contour reconstruction of AM parts based on light field imaging, belonging to the field of image reconstruction technology is provided. The method includes constructing an EPI-UNet framework, where a preset light field dataset is used to construct a training set, learning labels from the disparity maps corresponding to the preset light field dataset are obtained, and EPI-UNet framework is trained to obtain a predicted disparity vector with the training set and learning labels. Two mappings including disparity and depth mapping, and disparity and 3D mapping, are established to get 3D contour of the AM part. The experiments of validation for accuracy and 3D contour reconstruction of AM parts were performed. By applying light field multi-view vision to the AM process and combining the rich angle and spatial domain view information of the light field, this disclosure provides a reliable quality assurance for AM monitoring.

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Hubei, China

Shengnan Shen