Soft-Sensing Model of Deformation of Welded Steel Structure Based on FLS-SVM and its Application


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To effectively achieve rapid and high-precision measurements of the deformation of steel welded structure, multiple sets of the actual experimental data of steel welded structure are used as the samples, the soft-sensing model of deformation of welded steel structure, which uses the welding current I, the welding voltage U, the welding speed v and the flow of gas qm as arguments, is established by fuzzy least squares support vector machine, and adaptive genetic algorithm is used to optimize the number of positive gasification rules c and the parameters of kernel function σ, training, testing and practical application results show, the optimization of 200 steps, the training relative error which become saturated is 2.43%, the testing relative error is less than 2.45%.



Edited by:

Fangping Zhang




J. P. Lei and J. M. Chen, "Soft-Sensing Model of Deformation of Welded Steel Structure Based on FLS-SVM and its Application", Applied Mechanics and Materials, Vol. 628, pp. 152-156, 2014

Online since:

September 2014




* - Corresponding Author

[1] SHU Xian-qing,HUANG Xin-ming,DAI Guo-wen. Method of controlling weld distortion in steel structure manufacture. Electric Welding Machine, 2007, 37(6): 127-129.

[2] RUAN Xing-yi, BAO Ye-feng, SHI Zhong-xian. Measuring methods of welding deformation[J]. Electric Welding Machine, 2005, 35(5): 55-57.

[3] Zhu X, Ding H. Flatness tolerance evaluation: An approximate minimum zone solution[J]. CAD Computer Aided Design, 2002, 34(9): 655-664.

[4] Lee M. An enhanced convex-hull edge method for flatness tolerance evaluation[J]. Computer-Aided Design, 2009, 41(12): 930-941.

[5] Cui C, Li B, Huang F, et al. Genetic algorithm-based form error evaluation[J]. Measurement Science and Technology, 2007, 18(7): 1818-1822.

[6] Vapnik V. The nature of statistical learning theory[M]. New York: Springer, (1999).

[7] Isabelle G Y, Jason W, Stephen B. Gene Selection for Cancer Classification using Support Vector Machines[J]. Machine Learning, 2002, 46: 389-422.

[8] Vapnik V. Statistical learning theory[M]. New York: Wiley, (1998).

[9] Willis M. J. Artificial neural networks in process engineering[J]. IEE Proceedings-D, 1991, 138(3): 256-266.

[10] E Jiaqiang. Intelligent fault diagnosis and its application[M]. Changsha: Hunan University Press, 2006. (in Chinese).

[11] ZUO Hong-yan, LUO Zhou-quan, GUAN Jialin, WANG Yi-wei. Identification on rock and soil parameters for vibration drilling rock in the metal mine based on the fuzzy least square support vector machine[J]. Journal of Central South University, 2014, 21(3): 1085-1090.