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"A digital shadow approach for enhancing process monitoring in wire arc" by Haochen Mu, Fengyang He et al.

With the development of Industry 4.0 and smart manufacturing, improving production automation, intelligence, and digitalization has become a research trend in the Wire Arc Additive Manufacturing (WAAM) field. This study introduces a digital shadow that aims to improve the adaptiveness and dimensionality of monitoring systems in WAAM. Three sensors are used in the digital shadow: a welding electric signal sensor, a camera, and a laser profilometer to collect welding current and voltage data, image data, and point cloud data. The collected multi-scaled data are time and spatially synchronized by sampling multiple points along the welding path. Three ML algorithms are used for decision-making: Multi-layer Perceptron (MLP) classifier and YOLOv5 are used for time and spatial-scale detection, respectively, and a Variational Autoencoder (VAE) is used for the decision-level fusion. The system performance is then tested to detect defects and geometric errors in practical experiments and the res ....

Wire Arc Additive Manufacturing , Multi Layer Perceptron , Variational Autoencoder , Defect Detection , Digital Shadow , Image Processing , Aser Profilometer , Machine Learning Ml , Sensor Fusion , Elding Electric Signals , Ire Arc Additive Manufacturing Waam ,

iTWire - How IT leaders can avoid the uncertainty trap and make the right AI investments

GUEST OPINION:  Australian IT leaders are not having an easy time in 2024. They are facing waves of uncertainty and risk, leading to one of. ....

Artificial Intelligence , Machine Learning , Endava Australia , Artifical Intelligence Ai , Machine Learning Ml , Fit Leaders , Digital Transformation ,