Sensors array technique for monitoring aluminum alloy spot welding

Rui Wang , Zhen Luo , Ping Shan , Xianzheng Bu , Shuxian Yuan , Sansan Ao

Transactions of Tianjin University ›› 2010, Vol. 16 ›› Issue (5) : 322 -327.

PDF
Transactions of Tianjin University ›› 2010, Vol. 16 ›› Issue (5) : 322 -327. DOI: 10.1007/s12209-010-1423-1
Article

Sensors array technique for monitoring aluminum alloy spot welding

Author information +
History +
PDF

Abstract

In this paper, the sensors array technique is applied to the quality detection of aluminum alloy spot welding. The sensors array has three forms, i.e., linear magnetic sensors array, annular magnetic sensors array and cross magnetic sensors array. An algorithm based on principal component analysis is proposed to extract the signal eigenvalues. The three types of magnetic sensors array are used in the experiment of monitoring the signal. After the eigenvalues are extracted, they are used to build a relationship with the nugget information. The result shows that when the distance between the core of the array and the pole is 60 mm, the arrays work best. In this case, when the eigenvalues’ range of the linear array is 0.006 5–0.015 1, the quality of the spots is eligible. To the annular and cross array, when the ranges are 0.082 9–0.131 6 and 0.085 1–0.098 2 respectively, the nugget quality is eligible.

Keywords

spot welding / magnetic field / sensors array / principal component analysis

Cite this article

Download citation ▾
Rui Wang, Zhen Luo, Ping Shan, Xianzheng Bu, Shuxian Yuan, Sansan Ao. Sensors array technique for monitoring aluminum alloy spot welding. Transactions of Tianjin University, 2010, 16(5): 322-327 DOI:10.1007/s12209-010-1423-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Miller W. S., Zhang L., Bottenna J., et al. Recent development in aluminum alloys for the automotive industry[J]. Materials Science and Engineering, 2000, 280(1): 37-49.

[2]

Zhang Z., Li D., Zhao H., et al. Effects of modeling means on properties of monitoring models of spot welding quality[J]. China Welding, 2002, 11(2): 119-123.

[3]

He J., Liu Zhong. Two-dimensional direction finding of acoustic sources by a vector sensor array using the propagator method[J]. Signal Processing, 2008, 88(10): 2492-2499.

[4]

Wang Wensheng. The application of InSb magnetic sensor[J]. Sensor World, 1998, 4(10): 23-33.

[5]

Duan Jinsheng. Study on the weak signal detection method based on the wavelet transform[J]. SCI/TECH Information Development & Economy, 2007, 17(1): 96-97.

[6]

Jutten C., Herault J. Blind separation of source. Part I: An adaptive algorithm based on neuromimetic[J]. Signal Processing, 1991, 24(1): 1-10.

[7]

Delfosse N., Loubaton P. Adaptive blind separation of independent sources: A deflation approach[J]. Signal Processing, 1995, 45(1): 59-83.

[8]

Simon Haykin. Unsupervised Adaptive Filtering. Volume I: Blind Separation[M]. 2000, London: John Wiley & Sons.

[9]

Cichockia A., Thawonmas R., Amari S. Sequential blind signal extraction in order specified by stochastic properties[J]. Electronics Letter, 1997, 33(1): 64-65.

[10]

Li Y., Wan Jun. Sequential blind extraction of instantaneously mixed sources[J]. IEEE Transactions on Signal Processing, 2002, 50(5): 997-1006.

AI Summary AI Mindmap
PDF

112

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/