Weld surface defect detection based on improved two-dimensional principal component analysis

ZHOU Zhaoyi; ZHANG Yanan; WANG Xiaofeng; LIU Jun

doi:10.12073/j.hjxb.20210412001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2021 > 42(11): 70-76. > DOI: 10.12073/j.hjxb.20210412001

ZHOU Zhaoyi, ZHANG Yanan, WANG Xiaofeng, LIU Jun. Weld surface defect detection based on improved two-dimensional principal component analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(11): 70-76. DOI: 10.12073/j.hjxb.20210412001

Citation:

PDF (6874 KB)

Weld surface defect detection based on improved two-dimensional principal component analysis

ZHOU Zhaoyi^1,,
ZHANG Yanan¹,
WANG Xiaofeng^{1, 2, ,},
LIU Jun^{1, 2}

1.
Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, Tianjin University of Technology, Tianjin, 300384, China
2.
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin, 300384, China

More Information

Received Date: April 11, 2021
Available Online: December 30, 2021

Graphical Abstract

Abstract

Abstract

Robot welding inevitably brings various weld defects due to the irregularity of part shape and the complexity of welding process. When applying two-dimensional principal component analysis to the detection of weld surface defects, it often faces problems such as high computational complexity, low classification accuracy and inability to incrementally learn. To resolve these problems, a mean updated incremental two-dimensional principal component analysis (MUI2DPCA) algorithm is proposed. MUI2DPCA and feedforward neural network (FNN) are combined to detect weld surface defects online. Firstly, the local block images are obtained by preprocessing the video frame images captured by the camera. Then, the pattern features of weld block images are extracted online by MUI2DPCA. The algorithm performs incremental iterative estimation on principal components of block images, significantly reduces the computational complexity, and incrementally updates the current sample mean to reduce the influence of irrelevant feature changes on the convergences of principal components. Finally, FNN is used to establish the relationship between pattern features and weld categories, and the weld defect detection information is obtained in real time. The test results show that the average classification accuracy of the detection method is 95.40%, and the average processing speed reaches 29 frames per second, and it can meet the real-time requirements of weld detection.

FullText(HTML)

References (23)

References

Hou W H, Zhang D S, Wei Y, et al. Review on computer aided weld defect detection from radiography images[J]. Applied Sciences-Basel, 2020, 10(5): 1878. doi: 10.3390/app10051878

Kermorgant O. A magnetic climbing robot to perform autonomous welding in the shipbuilding industry[J]. Robotics and Computer- Integrated Manufacturing, 2018, 53: 178 − 186. doi: 10.1016/j.rcim.2018.04.008

于朋, 刚铁. 基于全聚焦成像技术的焊缝近表面平面类缺陷检测[J]. 焊接学报, 2019, 40(12): 36 − 40.

Yu Peng, Gang Tie. Use total focusing method to image the near-surface planar defect in welded joint[J]. Transactions of the China Welding Institution, 2019, 40(12): 36 − 40.

Sun J, Li C, Wu X J, et al. An effective method of weld defect detection and classification based on machine vision[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6322 − 6333. doi: 10.1109/TII.2019.2896357

马增强, 钱荣威, 许丹丹, 等. 线结构光焊接图像去噪方法[J]. 焊接学报, 2021, 42(2): 8 − 15. doi: 10.12073/j.hjxb.20200519002

Ma Zengqiang, Qian Rongwei, Xu Dandan, et al. Denoising of line structured light welded seams image based on adaptive top-hat transform[J]. Transactions of the China Welding Institution, 2021, 42(2): 8 − 15. doi: 10.12073/j.hjxb.20200519002

胡宏伟, 张婕, 彭刚, 等. 基于LBP-KPCA特征提取的焊缝超声检测缺陷分类方法[J]. 焊接学报, 2019, 40(6): 34 − 39.

Hu Hongwei, Zhang Jie, Peng Gang, et al. Defect classification for ultrasonic inspection in weld seam based on LBP-KPCA feature extraction[J]. Transactions of the China Welding Institution, 2019, 40(6): 34 − 39.

Zhang L, Zhang Y J, Dai B C, et al. Welding defect detection based on local image enhancement[J]. IET Image Processing, 2019, 13(13): 2647 − 2658. doi: 10.1049/iet-ipr.2018.5840

Shao W J, Huang Y, Zhang Y. A novel weld seam detection method for space weld seam of narrow butt joint in laser welding[J]. Optics and Laser Technology, 2018, 99: 39 − 51. doi: 10.1016/j.optlastec.2017.09.037

Oh S J, Jung M J, Lim C, et al. Automatic detection of welding defects using faster R-CNN[J]. Applied Sciences-Basel, 2020, 10(23): 8629. doi: 10.3390/app10238629

Zhang Z F, Wen G R, Chen S B. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding[J]. Journal of Manufacturing Processes, 2019, 45: 208 − 216. doi: 10.1016/j.jmapro.2019.06.023

Wang Y, Guo H. Weld defect detection of X-ray images based on support vector machine[J]. IETE Technical Review, 2014, 31(2): 137 − 142. doi: 10.1080/02564602.2014.892739

Chu H H, Wang Z Y. A vision-based system for post-welding quality measurement and defect detection[J]. International Journal of Advanced Manufacturing Technology, 2016, 86(9-12): 3007 − 3014. doi: 10.1007/s00170-015-8334-1

Jiang H Q, Zhao Y L, Gao J M, et al. Weld defect classification based on texture features and principal component analysis[J]. Insight, 2016, 58(4): 194 − 199. doi: 10.1784/insi.2016.58.4.194

Cruz F C, Simas E F, Albuquerque M C S, et al. Efficient feature selection for neural network-based detection of flaws in steel welded joints using ultrasound testing[J]. Ultrasonics, 2017, 73: 1 − 8. doi: 10.1016/j.ultras.2016.08.017

He K F, Li X J. Time-frequency feature extraction of acoustic emission signals in aluminum alloy MIG welding process based on SST and PCA[J]. IEEE Access, 2019, 7: 113988 − 113998. doi: 10.1109/ACCESS.2019.2935117

Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131 − 137. doi: 10.1109/TPAMI.2004.1261097

Esmaeili M, Ahmadi M, Kazemi A. Kernel-based two-dimensional principal component analysis applied for parameterization in history matching[J]. Journal of Petroleum Science and Engineering, 2020, 191: 107134. doi: 10.1016/j.petrol.2020.107134

Bi P F, Xu J, Du X, et al. l2, p-norm sequential bilateral 2DPCA: a novel robust technology for underwater image classification and representation[J]. Neural Computing and Applications, 2020, 32(22): 17027 − 17041. doi: 10.1007/s00521-020-04936-1

Can J, Zhang F, Wang J J, et al. Robust principal component analysis with intra-block correlation[J]. Neurocomputing, 2020, 386: 165 − 178. doi: 10.1016/j.neucom.2019.12.092

Wang Q Q, Gao Q X, Gao X B, et al. Optimal mean two-dimensional principal component analysis with F-norm minimization[J]. Pattern Recognition, 2017, 68: 286 − 294. doi: 10.1016/j.patcog.2017.03.026

Weng J Y, Zhang Y L, Hwang W S. Candid covariance-free incremental principal component analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 1034 − 1040. doi: 10.1109/TPAMI.2003.1217609

Ge W M, Sun M Y, Wang X F. An incremental two-dimensional principal component analysis for object recognition[J]. Mathematical Problems in Engineering, 2018(3): 1 − 13.

Li Yongyan, Zhao Weimin, Xue Haitao, et al. Defect recognition of resistance spot welding based on artificial neural network[C]// Proceedings of 2010 International Conference on Services Science, Management and Engineering. Berlin, Heidelberg: Springer, 2012, 115: 423-430.

[1]	YU Huiping, FENG Feng, ZHANG Yiliang, ZHAO Erbing. Numerical analysis of elimination stainless steel welding residual stress by over load tension[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(8): 119-123.
[2]	CHEN Zhanglan, XIONG Yunfeng. Numerical analysis on deformation of welded construction[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (5): 77-80.
[3]	YOU Min, LI Zhi, ZHAO Meirong, GUO Bin, YAN Jialing. Numerical analysis on stress distribution in adhesive-welded double lap joint of aluminum[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (11): 13-16.
[4]	LEI Yucheng, WANG Jian, ZHU Bin. Numerical analysis on N₂-Ar plasma welding arc[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (8): 17-20.
[5]	LI Bo, WU Jiefeng. Numerical analysis for cutting plasma arc[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2007, (9): 95-98.
[6]	LEI Yong-ping, HAN Feng-juan, Xia Zhi-dong, FENG Ji-cai. Numerical analysis of residual stress in ceramics/metal brazed joints[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (5): 33-36,41.
[7]	Lü Jian-min, CHEN Huai-ning, LIN Quan-hong.. Numerical analysis of a method for relieving welding stresses of girth-weld pipes with small diameters[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (4): 83-86.
[8]	HE Peng, FENG Ji-cai, QIAN Yi-yu, HAN Jie-chai, MAI Han-hui, JIA jin-guo. Numeric Analysis for Density Distribution of Element at the Interface in Diffusion Bonding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2002, (3): 80-82.
[9]	Dong Piming, Gu Fuming, Gao Jinqiang, Wang Erde, Tian Xitang. Numerical Analysis for Effect of Longitudinal Shrinkage of PerpendicularIntersection Weld on Flange Plane[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1999, (2): 132-138.
[10]	Zhao Xihua, Wu Lin, Liu Shubin, He Bing. Numerical analysis on force-amplification mechanism of welding tongs in spot-welding robots[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1993, (1): 35-39.

Cited By

Cited by

Periodical cited type(1)

杨书搏，刘元义，于圣洁，张悦，王丽娟，宋心宇. 基于逆向工程的设施农业就地翻土犁设计与试验. 中国农机化学报. 2023(12): 60-65 .

Other cited types(1)

Get Citation

PDF

XML

Article views (295) PDF downloads (30) Cited by(2)

Turn off MathJax

Article Contents

Abstract

References

Weld surface defect detection based on improved two-dimensional principal component analysis