Estimation of weld penetration using parameterized three-dimensional weld pool surface in gas tungsten arc welding

A skilled welder determines the weld joint penetration from his/her observation on the weld pool surface during the welding process. This paper addresses the estimation of weld joint penetration (i.e. determining back-side bead width that measures the degree of the weld joint penetration in full penetration welding) using the parameterized 3D weld pool surface in gas tungsten arc welding (GTAW). To this end, an innovative machine vision system is used to measure the specular weld pool surface in real-time. Various experiments under different welding conditions have been performed to produce full penetration welds with different back-side bead widths and acquire corresponding images for reconstructing the weld pool surface and calculating candidate estimation parameters. Through the least squares algorithm based statistic analyses, it was found that the width, length, and convexity of the 3D weld pool surface provides the optimal model to predict the back-side bead width with an acceptable accuracy. A foundation is thus established to effectively extract information from the weld pool surface to facilitate a feedback control of the weld joint penetration.

[1]  Nikhil R. Pal,et al.  Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool , 2008 .

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  Dragana Brzakovic,et al.  Weld pool edge detection for automated control of welding , 1991, IEEE Trans. Robotics Autom..

[4]  YuMing Zhang,et al.  Real-Time Sensing of Sag Geometry During GTA Welding , 1997 .

[5]  Shanben Chen,et al.  Computer vision sensing and intelligent control of welding pool dynamics , 2004 .

[6]  YuMing Zhang,et al.  Infrared sensing of full penetration state in gas tungsten arc welding , 1994 .

[7]  David E. Hardt,et al.  ULTRASONIC MEASUREMENT OF WELD PENETRATION , 1984 .

[8]  Y. M. Zhang,et al.  Weld pool surface depth measurement using a calibrated camera and structured light , 2007 .

[9]  Tao Lin,et al.  Binocular vision system for both weld pool and root gap in robot welding process , 2010 .

[11]  Radovan Kovacevic,et al.  Sensitivity of Front-Face Weld Geometry in Representing the Full Penetration , 1992 .

[12]  Jianqiang Yi,et al.  Extraction of Three-Dimensional Parameters for Weld Pool Surface in Pulsed GTAW With Wire Filler , 2003 .

[13]  Shanben Chen,et al.  Visual sensing and penetration control in aluminum alloy pulsed GTA welding , 2009 .

[14]  YuMing Zhang,et al.  Error Analysis of a Three-Dimensional GTA Weld Pool Surface Measurement System The proposed system, which uses specular reflection of the pool surface as a means of measurement, was shown to be accurate , 2009 .

[15]  Bryan A. Chin,et al.  Control of the welding process using infrared sensors , 1992, IEEE Trans. Robotics Autom..

[16]  Radovan Kovacevic,et al.  Real-Time Image Processing for Monitoring of Free Weld Pool Surface , 1997 .

[17]  Y. Zhang,et al.  Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process , 2003, J. Intell. Robotic Syst..

[18]  Miriam Zacksenhouse,et al.  Weld Pool Impedance Identification for Size Measurement and Control , 1983 .

[19]  YuMing Zhang,et al.  Sensing and Control of Weld Pool Geometry for Automated GTA Welding , 1995 .

[20]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[21]  N. M. Carlson,et al.  Ultrasonic sensing of weld pool penetration , 1988 .

[22]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.