Research Status of Intelligent Online Monitoring for Wire Arc Additive Manufacturing
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China
| Abstract: | Wire arc additive manufacturing (WAAM), owing to its advantages of high deposition efficiency, low cost, and low limitations on part dimensions, exhibits significant potential in the rapid fabrication of large and complex metal components. However, issues such as difficulties in geometrical accuracy control, internal micro-defects (e.g., porosity, cracks), and anisotropy of mechanical properties caused by the cyclic thermal history during layer-by-layer deposition severely restrict the industrial application and reliability of this technology. In response to the strongly coupled, nonlinear, and randomly disturbed characteristics of WAAM process parameters, intelligent online monitoring has become a key approach to improving manufacturing quality. This paper first reviews the diverse sensing schemes for WAAM process information monitoring, covering vision-based (visible light, laser, infrared, X-ray), acoustic emission, and arc spectroscopy methods, and comparatively analyzes the performance boundaries of each technique in capturing melt pool morphology and internal quality features. Subsequently, the research progress of machine learning (ML) in signal feature extraction and state classification is discussed, with emphasis on the robustness of deep neural networks and support vector machines in defect identification and process prediction. On this basis, intelligent control strategies based on data-driven models and reinforcement learning algorithms are summarized, and their application value in achieving adaptive control of key parameters such as droplet transfer and wire feed speed is explored. Finally, in view of current bottlenecks including difficulties in multi-source data fusion, scarcity of high-quality sample datasets, and weaknesses in multivariable cooperative control, the development trends of intelligent WAAM manufacturing are prospected. |
| Keywords: | WAAM; Online Monitoring; Vision Sensing; Machine Learning; Intelligent Control |
| DOI: | 10.57237/j.cst.2026.02.004 |
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Received: 30 April 2026
Accepted: 15 May 2026
Published Online: 19 May 2026
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