Recognition of Weld Penetration During K-TIG Welding Based on Acoustic and Visual Sensing
- 112 Downloads
In the field of welding process control, on-line monitoring of welding quality based on multi-sensor information fusion has attracted more attention. In order to recognize the penetration state of the Keyhole mode Tungsten Inert Gas welded joint in real time, an acoustic and visual sensing system was established in this paper. The acoustic and visual features that characterize the penetration state of the welded joints in 34 dimensions were extracted and the variation of the acoustic signal and the keyhole geometry were analyzed. In addition, the weighted scoring criterion based on the Fisher distance and the maximum information coefficient (Fisher–MIC) and Support Vector Machine (SVM) model based on cross-validation (CV) are designed as the feature selection method. The feature selection method can evaluate the penetration recognition accuracy of different feature subsets. The experiment results show that the maximum recognition accuracy was 97.1655%, which was performed by the 10-dimension optimal feature subset and the CV–SVM model with particle swarm optimization (PSO–CV–SVM). It is proved that the selected acoustic and visual features can well characterize the penetration state of the welded joints, and the feature selection method and PSO–CV–SVM model have superior performance.
KeywordsK-TIG welding Weld penetration Acoustic sensing Visual sensing Feature selection
This project was finically supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2015B010919005), Science and Technology Planning Project of Guangzhou City (Grant No. 201604046026), and National Natural Science Foundation of China (Grant No. 51374111).
- 1.Zhang, S. B., & Zhang, Y. M. (2001). Efflux plasma charge-based sensing and control of joint penetration during keyhole plasma arc welding. Welding Journal, 80(7), 157s–162s.Google Scholar
- 5.Han, G. M., Yun, S. H., Cao, X. H., & Li, J. Y. (2004). Acquisition and pattern recognition of spectrum information of welding metal transfer. Chinese Journal of Mechanical Engineering, 24(3), 699–703.Google Scholar
- 9.Čudina, M., Prezelj, J., & Polajnar, I. (2008). Use of audible sound for on-line monitoring of gas metal arc welding process. Metalurgija, 47(2), 81–85.Google Scholar
- 10.Tarn, J., & Huissoon, J. (2005). Developing psycho-acoustic experiments in gas metal arc welding. In IEEE International Conference on Mechatronics and Automation, 2005 (pp. 1112–1117 Vol. 1112).Google Scholar
- 14.Ma, J., Susca, S., Bajracharya, M., Matthies, L., Malchano, M., & Wooden, D. (2012). Robust multi-sensor, day/night 6-DOF pose estimation for a dynamic legged vehicle in GPS-denied environments. In IEEE International Conference on Robotics and Automation (pp. 619–626).Google Scholar
- 20.Lee, S. S., Kim, T. H., Hu, S. J., Cai, W. W., Li, J., & Abell, J. A. (2012). Characterization of joint quality in ultrasonic welding of battery tabs. Journal of Manufacturing Science and Engineering, 135(2), 2186–2199.Google Scholar
- 22.Tam, J. (2008). Methods of characterizing gas-metal arc welding acoustics for process automation. Waterloo: University of Waterloo.Google Scholar
- 26.Zhang, Y. M., & Zhang, S. B. (1999). Observation of the keyhole during plasma arc welding. Welding Journal, 78(2), 53S–58S.Google Scholar