Investigates real-time defect detection in wire arc additive manufacturing, suggesting a unified monitoring approach.
This study presents a methodology for real-time defect detection in Wire Arc Additive Manufacturing (WAAM) based on electromagnetic radiation emissions. Optical Emission Spectroscopy (OES) and Radio Emission Spectroscopy (RES) were combined to monitor plasma behavior during WAAM deposition of ER90S steel, Inconel 718, ER316L stainless steel, and CuAg alloy. Controlled interruptions of shielding gas flow were introduced to generate process instabilities and evaluate their influence on optical and radio-frequency emissions. A synchronized multi-sensor system integrating optical spectroscopy, radio-frequency analysis, high-speed imaging, and electrical measurements were employed. Results demonstrated that radio-frequency signatures are independent of welding parameters, material and shielding gas chemistries, with these factors affecting only signal intensity. The characteristic frequencies were instead governed by the welding process and its electrical behavior, enabling the development of a unified monitoring strategy for defect detection across different WAAM conditions. In contrast, optical emissions were strongly influenced by material and shielding gas compositions, while remaining insensitive to variations in welding parameters. Despite this dependency, common wavelength regions were identified across multiple materials and operating conditions, allowing the definition of robust optical monitoring criteria. The proposed methodology successfully detected process instabilities associated with shielding gas interruptions and demonstrated the complementary advantages of RES and OES. The main contribution of this work is the first application of RES for WAAM monitoring, establishing radio-frequency spectroscopy as a process-independent approach for real-time defect detection. The findings support the implementation of non-intrusive, low-latency monitoring systems for improved process robustness and quality assurance in industrial additive manufacturing.
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Serrati et al. (2026) studied this question.
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