@article{Casarini_P. Coelho_T. Olívio_Braz-César_Ribeiro_2020, title={Optimization and Influence of GMAW Parameters for Weld Geometrical and Mechanical Properties Using the Taguchi Method and Variance Analysis}, volume={5}, url={https://knepublishing.com/index.php/KnE-Engineering/article/view/7097}, DOI={10.18502/keg.v5i6.7097}, abstractNote={<p>Gas metal arc welding is one of the arc fusion processes that is widely used in industry due to its high efficiency. The correct selection of the input parameters has direct influence on the weld quality and, with the control of those parameters, it is possible to reduce the amount of weld material, improve its properties and then increase the productivity of the process. This study intends to take a group of weld parameters and submit them to the optimization by the Taguchi Method and check the influence of those through a Variance Analysis (ANOVA). An L9 orthogonal array gathered three parameters (weld voltage, weld speed and weld torch angle) into three levels, then, with all combinations set and performed, the macrography and the transversal tensile strength test provided, respectively, the geometrical and the mechanical properties. The signal-to-noise ratios enable the optimization and the ANOVA provided the influence of the input parameters on the response parameters. The weld speed appeared as the most influent parameter for the weld geometry, contributing 63.54% to reinforcement, 66.36% to width and 66.94% to penetration, and the weld torch angle the most influent to the ultimate transversal tensile strength (41.39%). The optimum levels to the reinforcement are 22.4 [V], 400 [mm/min] and 30 [°], to the width 22.4 [V], 300 [mm/min], 0 [°], to the penetration 23.3 [V], 400 [mm/min], 0 [°] and, lastly, to the ultimate transversal tensile strength 24.1 [V], 200 [mm/min], 15 [°]. The Taguchi method showed to be suitable for this kind of problem and giving an efficient experiment design and good results.</p> <p><strong>Keywords: </strong>Taguchi method, Optimization, GMAW</p&gt;}, number={6}, journal={KnE Engineering}, author={Casarini, Arthur and P. Coelho, João and T. Olívio, Émillyn and Braz-César, Manuel and Ribeiro, João}, year={2020}, month={Jun.}, pages={781–794} }