Investigation of porosity in pulsed GTAW of aluminum alloys based on spectral and X-ray image analyses

Abstract Alternating current pulsed gas tungsten arc welding was conducted to study the effect of welding conditions on the formation of porosity. Two conditions involved in the preparation of base metals were discussed: the application of a groove and thickness of the welding root face. Spectral analyses based on data mining and empirical mode decomposition (EMD) were proposed to detect porosity. Spectral lines were identified accurately by data mining based on an improved K-medoids algorithm which ascertained the classification number using a geodesic minimum spanning tree. EMD was employed to eliminate effectively the influence of noise and a pulsed current on spectral intensity ratio, resulting in clear characteristic values used for detection of pores. X-ray analyses verified the proposed method and showed that the welding conditions significantly affected the size and distribution of pores. The results indicated that the weld quality was improved with the application of a groove. And the number of pores and pores area decreased significantly when the thickness of the welding root face was 2 mm.

[1]  M. Williams,et al.  3D imaging and quantification of porosity in electron beam welded dissimilar steel to Fe-Al alloy joints by X-ray tomography , 2016 .

[2]  Chein-I Chang,et al.  An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis , 2000, IEEE Trans. Inf. Theory.

[3]  H. J. Aval Microstructure and residual stress distributions in friction stir welding of dissimilar aluminium alloys , 2015 .

[4]  Deyong You,et al.  Detection of imperfection formation in disk laser welding using multiple on-line measurements , 2015 .

[5]  Cheng Yu,et al.  Porosity induced fatigue damage of laser welded 7075-T6 joints investigated via synchrotron X-ray microtomography , 2015 .

[6]  Jiyong Zhong,et al.  Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length , 2014 .

[7]  M. Rethmeier,et al.  Laser Beam Welding of Aluminum Alloys Under the Influence of an Electromagnetic Field , 2013 .

[8]  Yanling Xu,et al.  On-line monitor of hydrogen porosity based on arc spectral information in Al–Mg alloy pulsed gas tungsten arc welding , 2015 .

[9]  Wang Bao,et al.  Detection of GTA welding quality and disturbance factors with spectral signal of arc light , 2009 .

[10]  Weiqi Wang,et al.  A Quantitative Model of Keyhole Instability Induced Porosity in Laser Welding of Titanium Alloy , 2014, Metallurgical and Materials Transactions A.

[11]  Yao Liu,et al.  Microstructure and mechanical properties of aluminum 5083 weldments by gas tungsten arc and gas metal arc welding , 2012 .

[12]  Yanling Xu,et al.  Arc spectral processing technique with its application to wire feed monitoring in Al–Mg alloy pulsed gas tungsten arc welding , 2013 .

[13]  Yixiong Wu,et al.  Quantitative characterization of porosity in Fe–Al dissimilar materials lap joint made by gas metal arc welding with different current modes , 2014 .

[14]  Chunming Wang,et al.  Porosity in fiber laser formation of 5A06 aluminum alloy , 2010 .

[15]  R. Cuamatzi-Meléndez,et al.  3-D porosity in T-welded connections repaired by grinding and wet welding , 2015 .

[16]  A. Mistry,et al.  Understanding the Effect of Heat Input and Sheet Gap on Porosity Formation in Fillet Edge and Flange Couch Laser Welding of AC-170PX Aluminum Alloy for Automotive Component Manufacture , 2015 .

[17]  Thomas Graf,et al.  Understanding Pore Formation in Laser Beam Welding , 2011 .

[18]  Antonio Ancona,et al.  Spectroscopic monitoring of penetration depth in CO2 Nd:YAG and fiber laser welding processes , 2012 .

[19]  Di Wu,et al.  EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM , 2017 .

[20]  Z. Liu,et al.  Effect of defects on fatigue strength of GTAW repaired cast aluminum alloy , 2006 .

[21]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.