Publication

Abstract : The prime objective of this article is to obtain maximum weld seam positional accuracy and weld quality with varying weld gap values over weld cycles in laser and vision-sensor-assisted robotic arc welding. At first, a vision sensor is used to obtain the seam positions, so that there is no need to teach the robot manually. For a repeatable environment or a similarly designed weld path, the weld seam can then be found by a laser sensor; if there is any error position, it is then notified to a robot controller to move the robot tool center point to the actual position. After that, the output data of the laser sensor in terms of variation of weld gap is used to set the welding parameters at their optimal values by the application of fuzzy logic and NSGA-II algorithm to achieve maximum weld quality. A MOTOMAN MA1440A arc welding robot system is used for experimental investigation of the proposed approach. Furthermore, maximized weld quality in terms of the weld bead size, mechanical, and microstructure properties is achieved by the optimal setting of significant process parameters with respect to the weld gap value obtained as output from the laser sensor. The proposed approach adds more value to the automation process and offline quality improvement of robotic arc welding.