The use of teaching-learning based optimization technique for optimizing weld bead geometry as well as power consumption in additive manufacturing

Abstract

Quality of weld bead geometry (width and height of metal deposited) and power consumption are still big challenges to the manufacture to control them in gas metal arc based additive manufacturing. The present study is aimed to optimize weld bead geometry and also made an attempt to reduce power consumption. Experiments are conducted at different torch angles, currents, wire feed speeds and welding speeds. Experimental results for width, height and depth of weld bead, power consumption and arc force are collected. Finite element method based numerical simulation is performed for width of molten pool to study its effect on the width and height of the weld bead. The process parameters are optimized using teaching-learning based optimization technique for achieving optimum weld bead geometry and power consumption. The proposed methodology found two optimal working conditions. Based on the power consumption, the optimal working condition-I is selected as best optimal working condition with optimum bead geometry such as 6.014 mm of width and 4.083 mm of height. The optimum power consumption is found to be 2496 W which is around 17%–41% less than that of experiments carried out. The optimal working condition is as follows: 124 A of current, 76.8° of torch angle, 8.38 m/min of wire feed speed and 0.42 m/min of welding speed.