Abstract
In this paper, aiming at the online monitoring of weld quality in welding process, a real-time monitoring system based on visual sensing technology was proposed. Based on the SDM method, the edge features of molten pool image are extracted, and the features of molten pool area, width, half-length, and back width are further obtained. Based on the extracted image features and welding process parameters, a stochastic forest penetration model was built to classify and identify the penetration status in the welding process and to predict the weld back penetration width regression. The classification and recognition rate of random forest penetration model is 89.8%.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 51969001), the Guangxi Major Science and Technology Projects of China (No. GuikeAA17204030), the Guangxi Natural Science Foundation of China (No. 2018GXNSFAA138080), and the Guangxi Science and Technology Base and Talent Project of China (No. GuikeAD18281007).
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Han, J., Feng, Z., Jiao, Z., Han, X. (2021). The Research of Real-Time Welding Quality Detection via Visual Sensor for MIG Welding Process. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-33-6502-5_4
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DOI: https://doi.org/10.1007/978-981-33-6502-5_4
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