Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals

doi:10.1007/s12204-012-1344-3

上海交通大学学报（英文版） ›› 2012, Vol. 17 ›› Issue (6): 673-678.doi: 10.1007/s12204-012-1344-3

Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals

WANG Shuan-yuan (王双园), GONG Liang* (贡亮), LIU Cheng-liang (刘成良)

(State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, China)

出版日期:2012-12-31 发布日期:2012-12-30
通讯作者: GONG Liang* (贡亮) E-mail:gongliang mi@sjtu.edu.cn

Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals

WANG Shuan-yuan (王双园), GONG Liang* (贡亮), LIU Cheng-liang (刘成良)

(State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, China)

Online:2012-12-31 Published:2012-12-30
Contact: GONG Liang* (贡亮) E-mail:gongliang mi@sjtu.edu.cn

摘要/Abstract

摘要： 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.

关键词: resistance spot welding, self-organizing map (SOM), quality assessment, electrode displacement

Abstract: 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.

Key words: resistance spot welding, self-organizing map (SOM), quality assessment, electrode displacement

中图分类号:

TG 438

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]. 上海交通大学学报（英文版）, 2012, 17(6): 673-678.

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.

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Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals

Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals

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