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To support the increasing demand for smart manufacturing in wire arc additive manufacturing, such as digital twins, high-frequency real-time bead measurement is a long-standing challenge due to the protracted processing time of laser scans and vision-based approaches. This article introduces a pioneering approach for high-frequency, real-time bead geometry measurements. Utilizing high-frequency electric signal sensors, welding current, and voltage are captured. Time and frequency features are subsequently extracted and channeled into multilayer perceptron regressors to predict bead height and width. The model is trained using ground truth data derived from a laser profilometer. Furthermore, a feature dimension reduction algorithm coupled with an incremental learning framework is incorporated to optimize time efficiency and adaptability. Comprehensive practical experiments and a comparative analysis have been conducted. The results demonstrate that the proposed measurement system offers faster measuring speeds than vision-based methods while maintaining accuracy comparable to laser scanning techniques.
Mu et al. (Thu,) studied this question.