Analysis of spatters in laser welding with beam oscillation: A machine vision approach

Abstract Spatter formation is still an issue in welding, particularly in laser welding. This research takes up the topic and proposes a machine vision approach for spatter tracking in high speed image series. After a description of the multi hypothesis tracking method, in which a Kalman-filter is used for optimal object state estimation, the tracking is applied on high speed images from an experimental series on laser welding with beam oscillation using stainless steel. On the basis of its results three different spatter formation mechanisms could be identified and described. Those were spatter formation by material ablation, by periodic re-entry of the laser spot into the melt pool and by melt pool dynamics.

[1]  Peter Plapper,et al.  Mechanical characteristics of laser braze-welded aluminium–copper connections , 2013 .

[2]  Michael F. Zaeh,et al.  Contribution on modelling the remote ablation cutting , 2011 .

[3]  A. Kaplan,et al.  Spatter in laser welding , 2011 .

[4]  Dave F. Farson,et al.  Analysis and control of penetration depth fluctuations in single-mode fiber laser welds , 2009 .

[5]  S. Raman,et al.  Effect of beam oscillation on fatigue life of Ti–6Al–4V electron beam weldments , 2007 .

[6]  Alexander Kaplan,et al.  Laser welding: The spatter map , 2010 .

[7]  H. Mohrbacher,et al.  Advantages of using an oscillating laser beam for the production of tailored blanks , 1997, Other Conferences.

[8]  Genyu Chen,et al.  Observation of spatter formation mechanisms in high-power fiber laser welding of thick plate , 2013 .

[9]  Bopaya Bidanda,et al.  Development of a spatter index for automated welding inspection using computer vision , 1989 .

[10]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[11]  Eckhard Beyer,et al.  Opportunities to enlarge the application area of remote-cutting , 2009 .

[12]  Michael F. Zaeh,et al.  Material processing with remote technology revolution or evolution , 2010 .

[13]  Deyong You,et al.  Visual-based spatter detection during high-power disk laser welding , 2014 .

[14]  Fred A. Hamprecht,et al.  Sputter Tracking for the Automatic Monitoring of Industrial Laser-Welding Processes , 2008, IEEE Transactions on Industrial Electronics.

[15]  Berndt Brenner,et al.  Laser Beam Welding with High-Frequency Beam Oscillation: Welding of Dissimilar Materials with Brilliant Fiber Lasers , 2011 .

[16]  Michael F. Zaeh,et al.  Spatter Formation in Laser Welding with Beam Oscillation , 2013 .

[17]  Til Aach,et al.  Robust High-Speed Melt Pool Measurements for Laser Welding with Sputter Detection Capability , 2007, DAGM-Symposium.

[18]  Nicolosi Leonardo,et al.  Novel algorithm for the real time multi-feature detection in laser beam welding , 2012, ISCAS 2012.

[19]  Florian Hugger,et al.  Spatter formation in laser beam welding using laser beam oscillation , 2015, Welding in the World.

[20]  Deyong You,et al.  Monitoring of high-power laser welding using high-speed photographing and image processing , 2014 .

[21]  Akira Okada,et al.  Velocity and Angle of Spatter in Fine Laser Processing , 2012 .

[22]  Philipp A. Schmidt,et al.  Joining of lithium-ion batteries using laser beam welding: Electrical losses of welded aluminum and copper joints , 2012 .

[23]  A. Volgenant,et al.  A shortest augmenting path algorithm for dense and sparse linear assignment problems , 1987, Computing.

[24]  Florian Albert,et al.  Laser beam oscillation strategies for fillet welds in lap joints , 2014 .

[25]  Gunther Reinhart,et al.  Welding joint detection by calibrated mosaicking with laser scanner systems , 2015 .