Abstract
Sensor technology application is the key for intelligent welding process. Multiple sensors fusion has shown their significant advantages over single sensor which can only provide limited information. In this paper, a feature-level data fusion methodology was presented to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding by means of online arc sound, voltage and spectrum signals. Based on the developed algorithms in time and frequency domain, multiple feature parameters were successively extracted and selected from sound and voltage signals, while spectrum distribution of argon atoms related to seam penetration were carefully analyzed before feature parameters selection. After the synchronization of heterogeneous feature parameters, the feature-level-based data fusion was conducted by establishing a classifier using support vector machine and 10-fold cross validation. The test results indicate that multisensory-based classifier has higher accuracy i.e., 96.5873 %, than single sensor-based one in term of recognizing seam defects, like under penetration and burn through from normal penetration.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under the Grant Nos. 61374071 and 51275301. The authors would like to appreciate the support provided by Pro. E. Kannatey-Asibu at Mechanical Engineering Department, The University of Michigan, USA. We would also like to thank editor and reviewers to help improving this paper.
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Zhang, Z., Chen, S. Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals. J Intell Manuf 28, 207–218 (2017). https://doi.org/10.1007/s10845-014-0971-y
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DOI: https://doi.org/10.1007/s10845-014-0971-y