A digital shadow approach for enhancing process monitoring in wire arc additive manufacturing using sensor fusion

•The proposed digital shadow demonstrates improved performance for detecting multiple defects using sensor fusion algorithm.•Welding electric signals are crucial for analyzing the causes of defects but not sufficient for detecting defects.•Camera is excelling at detecting surface anomalies, while laser scanner is more effective in measuring geometric inaccuracy.•Sensor fusion algorithm can analyze the formation of defects by examining the correlation between different signal sources. 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 results show that the overall F1 score is 0.791, including detecting, classifying, and analyzing the cause of defects. Additionally, the total predicting time is within 0.5 s, which is suitable for an in-process monitoring system.