Optimization of Welding Process Parameters for Rotating Arc Welding Sensor Based on Genetic Algorithm

Jian Ping Jia; Hua Zhang; Hui Huang

doi:10.4028/www.scientific.net/MSF.575-578.769

Paper Titles

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The Electrical Contact Resistance in Resistance Welding Evaluated by Gleeble Testing Machine
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Numerical Modeling of High Power MAG Welding on the Plate
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Analysis of Arc Welded Thin-Walled Cylinders to Investigate the Effects of Welding Process Parameters on Residual Stresses
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Optimization of Welding Process Parameters for Rotating Arc Welding Sensor Based on Genetic Algorithm
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Numerical Simulation on Temperature Field in Laser Welding for Aluminum Alloy with Different Thickness
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Evaluation of Corrosion Property for Repair Welding Zone of Exhaust Valve
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Numerical Simulation of Pulsed Arc Welding by Melting Electrode
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Numerical Simulation of Hydrostatic Extruding Cu/Al Bimetal Clad through Equal-Strain Contour Concave Die
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Optimization of Welding Process Parameters for Rotating Arc Welding Sensor Based on Genetic Algorithm

Abstract:

The high speed rotating arc welding sensor (RAWS) driven by motor with air axis is widely used in seam tracking system. Using the characters of the RAWS, the purpose of this study based on Genetic Algorithm (GA) is to optimize welding process parameters of the RAWS for obtaining satisfied seam geometry. The output variables are penetration, width and height of the seam geometry. These output variables are determined by input variables, which are the welding current, welding voltage, welding speed, rotating arc radius, rotating arc frequency and rotating arc direction. Experimentations are made according to the optimal welding process parameters, and the result shows that the GA is an effective method to optimize the welding process parameters of the RAWS. The described computational methodology enables obtaining a seam with desired geometry.