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Journal of Intelligent Manufacturing

Erschienen in:

25.02.2014

A quantitative estimation technique for welding quality using local mean decomposition and support vector machine

verfasst von: Kuanfang He, Xuejun Li

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2016

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Abstract

The experimental nonlinear time series of welding current contain the arc feature information related to welding quality. The local mean decomposition (LMD) combining with the support vector machine (SVM) is put forward to quantitatively estimate the rationality of welding parameters and welding formation quality. The LMD is used to investigate the time–frequency distribution of arc energy, and the energy entropy is employed to quantitatively judge the welding arc characteristics related to welding quality. The collected current signal is decomposed into a number of product functions (PFs) by LMD. The energy entropy of each PF is calculated to establish the welding arc energy feature vectors, which are inputted into support vector machine classifier. The LMD combining with SVM can quantitatively estimate the time–frequency energy distribution characteristics of the arc current signal at different welding parameters and welding formation quality. Experimental results are provided to confirm the effectiveness of this approach to estimate the rationality of welding parameters and welding formation quality.

Vorheriger Artikel Real-time simulation of neural network classifications from characteristics emitted by acoustic emission during horizontal single grit scratch tests

Nächster Artikel A weighted-coupled network-based quality control method for improving key features in product manufacturing process

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Absi Alfaro, S. C., Carvalho, G. C., & da Cunhab, F. R. (2006). A statistical approach for monitoring stochastic welding processes. Journal of Materials Processing Technology, 175(2006), 4–14.CrossRef

Brezak, D., Majetic, D., Udiljak, T., & Kasac, J. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing, 23(2012), 797–809.CrossRef

Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(2012), 639–650.

Chapelle, O., & Vapnik, V. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(2002), 131–159.CrossRef

Chatterjee, S., Chatterjee, R., Pal, S., Pal, K., & Pal, S. K. (2012). Adaptive chirplet transform for sensitive and accurate monitoring of pulsed gas metal arc welding process. The International Journal of Advanced Manufacturing Technology, 60(2012), 111–125.CrossRef

Cho, Y., & Rhee, S. (2000). New technology for measuring dynamic resistance and estimating strength in resistance spot welding. Measurement Science and Technology, 11(2000), 1173–1178.CrossRef

Chokkalingham, S., Chandrasekhar, N., & Vasudevan, M. (2012). Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling. Journal of Intelligent Manufacturing, 23(2012), 1995–2001.CrossRef

Cullen, J. D., Athi, N., Al-Jader, M., Johnson, P., Al-Shamma’a, A. I., & Shaw, A. (2008). Multisensor fusion for on line monitoring of the quality of spot welding in automotive industry. Measurement, 41(2008), 412–423.CrossRef

Dickinson, D. W., Franklin, J. E., & Stanya, A. (1980). Characterization of spot welding behavior by dynamic electrical parameter monitoring. Welding Journal, 59(1980), 170s–176s.

Ekici, S. (2012). Support vector machines for classification and locating faults on transmission lines. Applied Soft Computing, 12(2012), 1650–1658.CrossRef

Flandrin, P., Amin, M., McLaughlin, S., & Torresani, B. (2013). Time–frequency analysis and applications. IEEE Signal Processing Magazine, 30(2013), 19–150.CrossRef

He, K., Zhang, Z., Xiao, S., & Li, X. (2013). Feature extraction of AC square wave SAW arc characteristics using improved Hilbert–Huang transformation and energy entropy. Measurement, 46(2013), 1385–1392.CrossRef

He, K. F., Li, Q., & Chen, J. (2013). An arc stability evaluation approach for SW AC SAW based on Lyapunov exponent of welding current. Measurement, 46(1), 272–278.CrossRef

He, K. F., Wu, J. G., & Wang, G. B. (2011). Time–frequency entropy analysis of alternating current square wave current signal in submerged arc welding. Journal of Computers, 6(2011), 2092–2097.

Hsu, W., Chiou, D., Chen, C., Liu, M., Chiang, W., & Huang, P. (2013). Sensitivity of initial damage detection for steel structures using the Hilbert–Huang transforms method. Journal of Vibration and Control, 19(2013), 857–878.CrossRef

Huang, W., & Kovacevic, R. (2011). A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. Journal of Intelligent Manufacturing, 22(2011), 131–143.CrossRef

Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(2003), 1667–1689.

Li, R. X. (2012). Quality monitoring of resistance spot welding based on process parameters. Energy Procedia, 14(2012), 925–930.

Manupati, V. K., Rohit Anand, J. J., Thakkar, Lyes Benyoucef, Garsia, Fausto P., & Tiwari, M. K. (2013). Adaptive production control system for a flexible manufacturing cell using support vector machine-based approach. The International Journal of Advanced Manufacturing Technology, 67(2013), 969–981.CrossRef

Pal, S., Pal, S. K., & Samantaray, A. K. (2008). Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process. Science and Technology of Welding and Joining, 13(2008), 638–645.CrossRef

Smith, J. S. (2005). The localmean decomposition and its application to EEG perception date. Journal of the Royal Society Interface, 2(2005), 443–454.CrossRef

Sun, Y., Bai, P., Sun, H-y, & Zhou, P. (2005). Real-time automatic detection of weld defects in steel pipe. NDT & E International, 38(2005), 522–528.CrossRef

Wu, C., & Tung, P. (2008). Application of genetic algorithm to external noise cancellation and compensation in automatic arc welding system. Journal of Intelligent Manufacturing, 19(2008), 249–256.CrossRef

Zhang, Z., Wang, Y., & Wang, K. (2013). Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 24(2013), 1213–1227.CrossRef

Zhiyong, L., Qiang, Z., Yan, L., & Xiaocheng, Y. (2013). An analysis of gas metal arc welding using the Lyapunov exponent. Materials and Manufacturing Processes, 28(2013), 213–219.CrossRef

Titel: A quantitative estimation technique for welding quality using local mean decomposition and support vector machine
verfasst von: Kuanfang He
Xuejun Li
Publikationsdatum: 25.02.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 3/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-014-0885-8

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