Abstract
The state of weld bead penetration is a crucial factor that affects the service performance of the welding products. Since arc sound signals contain abundant information of welding process, they were usually adopted to monitor the penetration states of weld bead online. However, the arc sound signals are susceptible to the environment noise, so they are seldom applied in industrial practice. In this study, a human auditory perception model was proposed to identify the penetration states in GTAW process. In this model, an auricle and middle ear transformation function were adopted firstly to remove partial noise in the arc sound signals. Then through simulating the functions of human ear basement membrane, a gamma-tone frequency resolution algorithm was used to decompose the arc sound signals into 64 channels. At last, based on the short-time energies of arc sound in these channels, the feature vectors were built to identify the penetration states. The experimental results show that the proposed method has high accuracy in recognition rates and strong anti-noise interference capabilities. The human auditory perception model proposed in this study has potential practical applications in industrial environment.
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Acknowledgements
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (51465043).
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Gao, Y., Wang, Q., Gong, Y., Huang, L. (2019). Weld Bead Penetration State Recognition in GTAW Process Based on a Human Auditory Perception Model. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-13-3651-5_5
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DOI: https://doi.org/10.1007/978-981-13-3651-5_5
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