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.

中文翻译：

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使用传感器融合增强电弧增材制造过程监控的数字阴影方法

随着工业4.0和智能制造的发展，提高生产自动化、智能化、数字化已成为电弧增材制造（WAAM）领域的研究趋势。本研究引入了数字阴影，旨在提高 WAAM 中监控系统的适应性和维度。数字阴影中使用了三个传感器：焊接电信号传感器、摄像头和激光轮廓仪，用于收集焊接电流和电压数据、图像数据和点云数据。通过沿焊接路径采样多个点，收集的多尺度数据在时间和空间上同步。三种机器学习算法用于决策：多层感知器（MLP）分类器和YOLOv5分别用于时间和空间尺度检测，变分自动编码器（VAE）用于决策级融合。然后对系统性能进行测试，在实际实验中检测缺陷和几何误差，结果显示总体F1得分为0.791，包括缺陷的检测、分类和原因分析。此外，总预测时间在0.5秒以内，适合过程监控系统。