To classify the quality of the resistance spot welding process, a relationship between the welder electrode
displacement curve characteristics and the weld shear force has been explored. Eleven statistical features of the
displacement signals are extracted to represent the welding quality. Self-organizing map (SOM) neural networks
have been employed to discover their quantitative relationship. In order to identify the influence of various
displacement curve features, all of the available combinations have been used as inputs for SOM neural networks.
Further we analyze the impact of each feature on the classification results, yielding the best quality-indicative
combination of characteristics. There is no determinant relationship between the welding quality and the level
of expulsion rate. The quality of welding is most impacted by the maximum electrode displacement, the span of
welding process and the centroid of the electrode displacement curve. The experiments show that SOM is feasible
to assess the welding quality and can render the visualized intuitive evaluation results.
WANG Shuan-yuan (王双园), GONG Liang* (贡亮), LIU Cheng-liang (刘成良)
. Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals[J]. Journal of Shanghai Jiaotong University(Science), 2012
, 17(6)
: 673
-678
.
DOI: 10.1007/s12204-012-1344-3
[1] Ministry of Industry and Information Technology of the China. Economic operation of China’s automotive industry from january to july in 2012 [EB/OL]. (2012-8-12) [2012-08-10]. http://www.miit. gov.cn/n11293472/n11293832/n11294132/n12858417/n12858612/14775276.html.
[2] Li W, Hu S J, Ni J. On-line quality estimation in resistance spot welding [J]. Journal of Manufacturing Science and Engineering, 2000, 122(3): 511-512.
[3] Cho H S, Chun D W. A microprocessor-based electrode movement controller for spot weld quality assurance [J]. IEEE Transactions on Industrial Electronics,1985. 32(3): 234-238.
[4] Lee S R, Choo Y J, Lee T Y, et al. A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm [J]. Journal of Manufacturing Systems, 2001, 20(5): 320-328.
[5] Farson D F, Ely K, Frech T. Monitoring resistance spot nugget size by electrode displacement [J]. Journal of Manufacturing Science and Engineering, 2004,126(2): 391-394.
[6] Zhang Peng-xian, Zhang Hong-jie, Chen Jian-hong. Quality monitoring of resistance spot welding based on electrode displacement characteristics analysis [J].Frontiers of Mechanical Engineering in China, 2007,2(3): 176-181 (in Chinese).
[7] Hamidinejad S M, Kolahan F, Kokabi A H. The modeling and process analysis of resistance spot welding on galvanized steel sheets used in car body manufacturing [J]. Materials and Design, 2012, 34(0): 759-767.
[8] Martn O, Lopez M, Martn F. Artificial neural networks for quality control by ultrasonic testing in resistance spot welding [J]. Journal of Materials Processing Technology, 2007, 183(2-3): 226-233.
[9] Zhang Hong-jie, Hou Yan-yan. Quality evaluation of the resistance spot welding based on PCA-SVM [J].Transactions of the China Welding Institution, 2009,30(4): 97-100 (in Chinese).
[10] Gong L, Liu C L, Li Y M, et al. Online weld quality benchmarking and assurance during the massproduction resistance spot welding process [J]. Advanced Materials Research, 2012, 443-444: 872-880.
[11] Kohonen T. Self-organized formation of topologically correct feature maps [J]. Biological Cybernetics, 1982,43(1): 59-69.
[12] Liu Y G, Weisberg R H, Mooers C N K. Performance evaluation of the self-organizing map for feature extraction [J]. Journal of Geophysical Research-Oceans, 2006, 111(C5): C05018.
[13] Suganthan P N. Pattern classification using multiple hierarchical overlapped self-organising maps [J]. Pattern Recognition, 2001, 34(11): 2173-2179.
[14] Rowlands H, Antony J. Application of design of experiments to a spot welding process [J]. Assembly Automation, 2003, 23(3): 273-279.
[15] Vesanto J, Alhoniemi E. Clustering of the selforganizing map [J]. IEEE Transactions on Neural Networks,2000, 11(3): 586-600.
[16] Liu Hai-ning. Sparse coding based machine condition recognition and its application in the condition moniting of a heavy roller grinder [D]. Shanghai: School of Mechanical Engineering, Shanghai Jiaotong University,2011: 53-56 (in Chinese).
[17] Gong L, Liu C L, Li Y M. Control criteria determination and quality inference for resistance spot welding through monitoring the electrode displacement using bayesian belief networks [J]. Journal of Advanced Mechanical Design, Systems and Manufacturing, 2012,6(4): 432-444.
