WAAM-ViD: towards universal vision-based monitoring for wire arc additive manufacturing

Abstract

In the context of Industry 4.0, autonomous and data-driven manufacturing processes are advancing rapidly, with wire arc additive manufacturing (WAAM) emerging as a promising technique for producing large-scale metal components. Ensuring quality control and part traceability in WAAM remains an area of active research, as existing process monitoring systems often require operator intervention and are tailored to specific machine setups and camera configurations, limiting adaptability across industrial environments. This study addresses these challenges by developing an angle-invariant melt pool analysis pipeline capable of recognising bead features in wire-based directed energy deposition from monitoring images captured using various camera qualities, positions, and angles. A new benchmark dataset, WAAM-ViD, is also introduced to support future research. The proposed pipeline integrates two deep learning models: DeepLabv3, fine-tuned through active learning for precise melt pool segmentation (Dice similarity coefficient of 95.90%), and WAAM-ViDNet, a regression-based multimodal model that predicts melt pool width using the segmented images and camera calibration data, achieving 88.71% accuracy. The results demonstrate the pipeline’s effectiveness in enabling real-time process monitoring and control in WAAM, representing a step toward fully autonomous and adaptable additive manufacturing systems.