Arc Spectral Characteristics Extraction Method in Pulsed Gas Tungsten Arc Welding for Al-Mg Alloy

Abstract

Abstract:

In this paper, aimed at the problems in arc spectral information in AlMg Alloy pulsed alternating current (AC) GTAW, principal component analysis (PCA) was utilized for redundancy removal and spectral characteristic signals extraction. The spectral wavelengths were classified as the first and the second principal components, associated with Ar I lines and metal lines respectively; pulse interference caused by welding current was eliminated by using mean value during signal’s pulse period (MSP) and wavelet reconstruction. The relations between these extracted signals and different defects were discussed. The results show that the first principal component is closely related to the arc heat input while the second principal component is closely related to the weld pool stability.

Key words: gas tungsten arc welding (GTAW), arc spectra, principal component analysis, characteristics extraction, welding defect

CLC Number:

TG 409

YU Huanwei,YE Zhen,ZHANG Zhifen,CHEN Huabin,CHEN Shanben. Arc Spectral Characteristics Extraction Method in Pulsed Gas Tungsten Arc Welding for Al-Mg Alloy

[J]. Journal of Shanghai Jiaotong University, 2013, 47(11): 1655-1660.

References

［1］陈善本. 焊接过程现代控制技术［M］. 哈尔滨, 哈尔滨工业大学出版社, 2001: 1720.

［2］Wang J J, Lin T, Chen S B. Obtaining weld pool vision information during aluminium alloy TIG welding［J］. Int J Adv Manuf Tech, 2005, 26(3): 219227.

［3］Xu Y, Yu H, Zhong J, et al. Realtime seam tracking control technology during welding robot GTAW process based on passive vision sensor［J］. J Mater Process Tech, 2012, 212(8): 16541662.

［4］Christner B, Lovell R, Campbell M. Developing a GTAW penetration control system for the Titan IV program［J］. Welding & Metal Fabrication, 1998, 66: 2938.

［5］樊丁，黄健康，石玗，等. 基于电弧声ARMA双谱分析对熔滴过渡类型SVM模式识别［J］. 上海交通大学学报, 2008, 42(增刊): 4245.

FAN Ding, HUANG Jiankang, SHI Yu, et al. Droplet transfer pattern recognition by SVM based on arc acoustic ARMA bispectrum analysis［J］. Journal of Shanghai Jiaotong University,2008,42(sup):4245.

［6］杨运强, 李俊岳, 胡胜钢, 等. 熔化极脉冲电弧焊熔滴过渡特征光谱信息及其应用［J］. 焊接学报, 2001(4): 3638.

YANG Yunqiang, LI Junyue, HU shenggang, et al. The characteristic spectral information of droplet transfer in pulsed MIG welding and it’s applications［J］. Transactions of the China Welding Institution, 2001(4): 3638.

［7］Park H, Sehun R, Kim D. A fuzzy pattern recognition based system for monitoring laser weld quality ［J］. Measurement Science and Technology, 2001(12): 13181324.

［8］Li P J, Zhang Y M. Precision sensing of arc length in GTAW based on arc light spectrum ［J］. J Manuf Sci ET Asme, 2001, 123 (1): 6265.

［9］Sforza P, de Blasiis D. Online optical monitoring system for arc welding ［J］. Independent Nondestructive Testing and Evaluation, 2002, 35(1): 3743.

［10］Mirapeix J, GarcíaAllende P B, Cobo A, et al. Realtime arcwelding defect detection and classification with principal component analysis and artificial neural networks［J］. Independent Nondestructive Testing and Evaluation, 2007, 40(4): 315323.

［11］宋永伦. 交流焊接电弧的若干机理与工程应用基础［J］. 电焊机, 2009, 39(1): 9799.

SONG Yonglun. Some understanding of the practical knowledge on A.C.TIG welding arc［J］. Electric Welding Machine, 2009, 39(1): 9799.

［12］杨晓红，宋永伦，胡坤平.交流TIG电弧过零时段的时间能量分布特征［J］. 焊接学报, 2009, 30(2): 2125.

YANG Xiaohong, Song Yonglun, Hu Kunping. Characteristic of timeenergy distribution at currentzero period on AC TIG welding arc［J］. Transactions of the China Welding Institution, 2009, 30(2): 2125

［13］Jolliffe I T. Principal Component Analysis ［M］. New York, Springer, 2002: 16.

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