Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting

GAO Changlin; SONG Yanli; ZUO Hongzhou; ZHANG Cheng

doi:10.12073/j.hjxb.20210515001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2022 > 43(1): 98-106. > DOI: 10.12073/j.hjxb.20210515001

GAO Changlin, SONG Yanli, ZUO Hongzhou, ZHANG Cheng. Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 98-106. DOI: 10.12073/j.hjxb.20210515001

Citation:

PDF (2762 KB)

Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting

GAO Changlin^1,,
SONG Yanli^{1, 2, 3, ,},
ZUO Hongzhou⁴,
ZHANG Cheng¹

1.
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China
2.
Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China
3.
Hubei Engineering Research Center for Green & Precision Material Forming, Wuhan University of Technology, Wuhan, 430070, China
4.
Hubei Qixing Automobile Body Co., Ltd., Suizhou, 441300, China

More Information

Received Date: May 14, 2021
Accepted Date: February 14, 2022
Available Online: February 18, 2022

Graphical Abstract

Abstract

Abstract

Considering the complex causes and various impact factors for welding defects, diagnosis methods based on artificial intelligence algorithms are regarded as one of the directions for the development of intelligentizing welding. In this study, an improved diagnosis method for welding defect based on PSO-BP neural network is proposed. Connection learning mechanism of neural network is used instead of the rule reasoning mechanism of traditional expert systems. It also makes adaptive adjustments to the PSO algorithm, introduced dynamic weight factors, and builds an adaptive PSO-BP neural network model. Compared with the traditional PSO-BP neural network model, the number of iterations required to train the improved PSO-BP neural network model reduced by 13.1%, the accuracy of diagnostic results increased from 93.3% to 96.7%, the precision increased from 91.3% to 98.3%, and the comprehensive performance index increased from 91.7% to 96.9%. The results show that the improved algorithm can significantly improve the efficiency and accuracy of welding defect diagnosis, and has good engineering application value.
- cause of welding defects,
- neural network,
- dynamic weighting,
- adaptive

FullText(HTML)

References (17)

References

吴叶军, 魏艳红. 智能化焊接CAPP的分析与开发[J]. 焊接学报, 2015, 36(7): 109 − 112.

Wu Yejun, Wei Yanhong. Analysis and development of intelligentialized welding CAPP system[J]. Transactions of the China Welding Institution, 2015, 36(7): 109 − 112.

Tsoukalas V D, Kontesis M, Badogiannis E, et al. WELDES: An intelligent defects expert system for aluminum welding process[J]. Wseas Transactions on Information Science & Applications, 2007, 4(2): 339 − 345.

宋燕利, 余成, 戴定国, 等. 基于BP 和GA 的激光焊接热源模型参数优化[J]. 塑性工程学报, 2017, 24(1): 218 − 222.

Song Yanli, Yu Cheng, Dai Dingguo, et al. Parameter optimization of heat source model for laser welding based on BP neural network and genetic algorithm[J]. Journal of Plasticity Engineering, 2017, 24(1): 218 − 222.

邵晴, 于庆斌, 尹华, 等. 焊接热输入对高速动车组转向架侧梁焊接变形的影响及优化[J]. 焊接学报, 2020, 41(12): 25 − 32,48. doi: 10.12073/j.hjxb.20200216002

Shao Qing, Yu Qingbin, Yin Hua, et al. Effect of welding heat input on welding deformation of bogie side beam of high-speed EMU and optimization[J]. Transactions of the China Welding Institution, 2020, 41(12): 25 − 32,48. doi: 10.12073/j.hjxb.20200216002

Liu J, Xu G C, Ren L, et al. Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network[J]. The International Journal of Advanced Manufacturing Technology, 2017, 90(9-12): 2581 − 2588. doi: 10.1007/s00170-016-9588-y

Li Z K, Zhao X H. BP artificial neural network based wave front correction for sensor-less free space optics communication[J]. Optics Communications, 2017, 385: 219 − 228. doi: 10.1016/j.optcom.2016.10.037

刘占军, 单宝峰, 贺平. 小波神经网络在铝合金焊接缺陷诊断中的研究[J]. 振动、测试与诊断, 2005, 25(3): 219 − 221. doi: 10.3969/j.issn.1004-6801.2005.03.013

Liu Zhanjun, Shan Baofeng, He Ping. Wavelet neural network application to diagnosing defect of aluminum alloy welding[J]. Journal of Vibration, Measurement & Diagnosis, 2005, 25(3): 219 − 221. doi: 10.3969/j.issn.1004-6801.2005.03.013

姜洪权, 贺帅, 高建民, 等. 一种改进卷积神经网络模型的焊缝缺陷识别方法[J]. 机械工程学报, 2020, 56(8): 235 − 242. doi: 10.3901/JME.2020.08.235

Jiang Hongquan, He Shuai, Gao Jianmin, et al. An improved convolutional neural network for weld defect recognition[J]. Journal of Mechanical Engineering, 2020, 56(8): 235 − 242. doi: 10.3901/JME.2020.08.235

Ravi R, Aaquib R K, Chirag P, et al. Classification and identification of surface defects in friction stir welding: An image processing approach[J]. Journal of Manufacturing Processes, 2016, 22: 237 − 253. doi: 10.1016/j.jmapro.2016.03.009

Bacioiu D, Melton G, Papaelias M, et al. Automated defect classification of aluminium 5083 TIG welding using HDR camera and neural networks[J]. Journal of Manufacturing Processes, 2019, 45: 603 − 613. doi: 10.1016/j.jmapro.2019.07.020

Zhang Z, Jia L M, Qin Y. Modified constriction particle swarm optimization algorithm[J]. Journal of Systems Engineering and Electronics, 2015, 26(5): 1107 − 1113.

张爱华, 高佛来, 牛小革, 等. 基于BP神经网络的钢轨闪光对焊接头灰斑面积预测[J]. 焊接学报, 2016, 37(11): 11 − 14.

Zhang Aihua, Gao Folai, Niu Xiaoge, et al. Prediction of gray-spot area in rail flash butt welded joint based on BP neural network[J]. Transactions of the China Welding Institution, 2016, 37(11): 11 − 14.

Avidan S. Ensemble tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261 − 271. doi: 10.1109/TPAMI.2007.35

Li J Y, Men C, Qi J F, et al. Impact factor analysis, prediction, and mapping of soil corrosion of carbon steel across China based on MIV-BP artificial neural network and GIS[J]. Journal of Soils and Sediments, 2020, 20(8): 3204 − 3216. doi: 10.1007/s11368-020-02649-5

刘碧瑶. 基于BP神经网络的住院费用建模研究[D]. 杭州: 浙江大学, 2006.

Liu Biyao. Research of establishing hospitalization charge fitting model by using BP neural network[D]. Hangzhou: Zhejiang University, 2006.

Sheela K G, Deepa S N. Review on methods to fix number of hidden neurons in neural networks[J]. Mathematical Problems in Engineering, 2013(6): 425740.

王东风, 孟丽. 粒子群优化算法的性能分析和参数选择[J]. 自动化学报, 2016, 42(10): 1552 − 1561.

Wang Dongfeng, Meng Li. Performance analysis and parameter selection of PSO algorithms[J]. Acta Automatica Sinica, 2016, 42(10): 1552 − 1561.

[1]	LAI Xuhui, XU Yan, ZHOU Jianping, Ilham ABDUREYIM null. Adaptive fault tolerant fusion interpolation algorithm based on RBF network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(10): 81-87. DOI: 10.12073/j.hjxb.2018390253
[2]	CHEN Hui, XUE Jiaxiang, HENG Gongchun, WANG Leilei. Integral parameter self-adjustment control algorithm in pulsed MIG welding power source[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(3): 97-100.
[3]	GAO Xiangdong, WU Jiajie, XIAO Zhenlin, CHEN Xiaohui. Seam tracking algorithm based on magneto-optical imaging and self-adaptive Kalmanfiltering[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(3): 9-12.
[4]	CHI Dazhao, GANG Tie, SUN Changli. An ultrasonic clutter suppression method using adaptive filter[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(11): 33-36.
[5]	SHEN Junqi, HU Shengsun, FENG Shengqiang. Application of adaptive median filtering in vision seam tracking[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (3): 57-60.
[6]	ZHANG Tao, GUI Weihua, WANG Suiping. Direct model reference adaptive control of gas metal arc welding process[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (4): 25-27.
[7]	LI Hong-ke, SHI Qing-yu, ZHAO Hai-yan, LI Ting. Auto-adapting heat source model for numerical analysis of friction stir welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2006, (11): 81-85.
[8]	YE Jian-xiong, ZHANG Hua, YANG Wu-qiang. Adaptive fuzzy controller based on variable universe for seam tracking[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2005, (12): 32-34.
[9]	MIAO Yu-gang, LIU Li-ming, WANG Ji-feng, ZHU Mei-li. Adaptive arc length control for magnesium alloy sheets TIG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (6): 33-36.
[10]	ZHANG Peng-xian, MA Yue-zhou, CHEN Jian-hong, LIANG Wei-dong. An on-line adaptive control system of CO₂ arc welding current waveform[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (5): 17-20,24.

Cited By

Cited by

Periodical cited type(5)

1.	吴鑫鑫，柏子涵，林伟坚，张清青. 薄壁零件成形制造中激光焊接及其数值仿真研究进展. 焊接技术. 2025(03): 1-7+145 .
2.	李岩，刘琪，李艳彪，张艳峰，田孟良，吴志生. 外部约束对薄壁高温合金尾喷管焊接变形的影响. 材料热处理学报. 2023(08): 175-185 .
3.	张景祺，林健，雷永平，肖荣诗，李龙，宋征. 栅格结构激光焊接失稳有限元分析. 航空制造技术. 2021(12): 61-69 .
4.	文龙. 钢结构工业厂房构件焊接应力及变形控制措施. 城市住宅. 2020(03): 165-166 .
5.	苏栋栋，王世伟，张昭辉，杨政豫. 难熔金属焊接温度场数值模拟及焊接影响因素研究. 甘肃科学学报. 2020(06): 84-90 .

Other cited types(12)

Get Citation

PDF

XML

Article views (364) PDF downloads (43) Cited by(17)

Turn off MathJax

Article Contents

Abstract

References

Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting