Abstract
Feature selection is the key issue for multisensory data fusion-based online welding quality monitoring in the area of intelligent welding process. This paper mainly focus on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of a series of analysis of synchronous online arc spectrum, arc sound pressure and arc voltage signal. Based on the developed feature selection algorithms, hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72 % of the established classification model support vector machine-cross validation (SVM-CV).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen S-B, Wu J (2009) Intelligentized methodology for arc welding dynamical processes. Springer, Berlin
Zhang W, Zhang Y (2012) Modeling of human welder response to 3D weld pool surface: part II—results and analysis. Weld J 91:329s–353s
Christner B (1998) Developing a GTAW penetration control system for the Titan IV program. Weld Metal fabrication 66:29–38
Kannatey-Asibu E Jr (2009) Principles of laser materials processing. Wiley, Hoboken
Chen W, Chin B (1990) Monitoring joint penetration using infrared sensing techniques. Weld J 69:181s–185s
Zhang Z, Yu H, Lv N, Chen S (2013) Real-time defect detection in pulsed GTAW of Al alloys through on-line spectroscopy. J Mater Process Technol 213:1146–1156
Chilian A, Hirschmuller H, Gorner M (2011) Multisensor data fusion for robust pose estimation of a six-legged walking robot. In: 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 2497–2504
Sun A, Kannatey-Asibu E Jr, Gartner M (2002) Monitoring of laser weld penetration using sensor fusion. J Laser Appl 14:114–121
Lee S (2013) Process and quality characterization for ultrasonic welding of lithium-ion batteries. University of Michigan, Ann Arbor
Garcia-Allende P, Mirapeix J, Conde O, Cobo A, López-Higuera J (2009) Spectral processing technique based on feature selection and artificial neural networks for arc-welding quality monitoring. NDT E Int 42:56–63
Shea JE, Gardner C (1983) Spectroscopic measurement of hydrogen contamination in weld arc plasmas. J Appl Phys 54:4928–4938
Li J, Song Y (1994) Spectral information of arc and welding automation. Weld World Lond 34:317–317
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. Pattern Anal Mach Intell IEEE Trans 19:153–158
Acknowledgment
This work is supported by the National Natural Science Foundation of China under the Grant No. 61374071 and the NDRC of China, under the Grant No. HT[2012]2144.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, Z., Chen, S. (2015). Data-Driven Feature Selection for Multisensory Quality Monitoring in Arc Welding. In: Tarn, TJ., Chen, SB., Chen, XQ. (eds) Robotic Welding, Intelligence and Automation. RWIA 2014. Advances in Intelligent Systems and Computing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-319-18997-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-18997-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18996-3
Online ISBN: 978-3-319-18997-0
eBook Packages: EngineeringEngineering (R0)