A study on MOORA-based taguchi method for optimization in automated GMA welding process

Generally, welding is an essential technique, which widely used in industries such as automobile, aerospace, shipbuilding, transportation, nuclear and offshore wind energy. The automatic GMA(Gas Metal Arc) welding process is a welding process that yields coalescence of metals by heating with a welding arc between a continuous filler metal electrode and the workpiece. A controller is used to control wire feed rate, arc current and welding voltage such as welding parameters. The welding processalso is a multi-objective, multi-factor metal fabrication technique. Several welding parameters interact in a complicated way, which could lead to direct or indirect effect on the bead geometry as welding quality. Therefore, welding parameters are required to be optimized for their responses or welding characteristics. Multi-Objective Optimization Method by Ratio Analysis (MOORA) has been used with great success in experimental designs for problems with multiple parameters due to its practicality. This paper proposed an intelligent algorithm with entropy measurement method for optimizing GMA welding process with multiple performance characteristics. The weights of the quality characteristics are determined by employing the entropy measurement method. Welding parameters are selected welding current, arc voltage, welding speed and CTWD (Contact Tip to Work Distance) and analyzed their effect on the three quality characteristics such as bead width, bead height and hardness of the weldments. Based on the Taguchi quality design theory, a L-9 orthogonal array was chosen for the experiments. The S/N (signal-to-noise) ratio was employed to predict the optimal parameter setting. The confirmation experiment verifies the proposed the developed MOORA with entropy measurement technology has the ability to predict the optimal welding parameters with multiple quality characteristics. (C) 2019 Elsevier Ltd. All rights reserved.