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Friction stir welded T-joints optimization
Affiliation:1. Instituto de Engenharia Mecânica e Gestão Industrial (INEGI), R. Dr. Roberto Frias, 400 4200-465 Porto, Portugal;2. Faculdade de Engenharia da Universidade do Porto (FEUP), R. Dr. Roberto Frias, s/n 4200-465 Porto, Portugal;1. Department of Materials Science, Fudan University, 220 Handan Road, Shanghai 200433, China;2. Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory for Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China;3. Department of Mechanical Engineering, The University of Manitoba, 15 Gillson Street, Winnipeg MB R3T 5V6, Canada
Abstract:The increasing use of aluminium alloys in transportation industry, such as railways, shipbuilding and aeronautics, promotes the development of more efficient and reliable welding processes. Friction stir welding (FSW) is a prominent solid-state joining technology that arose as a possible reliable welding solution. Optimized process parameters are not regularly used in previous studies found in the literature, in particular T-joints, which difficult the process industrial application. This study is focused on the optimization of friction stir welded T-joints using the Taguchi method. Mechanical tests of 27 different welded joints were carried out, and results were analysed using ANOVA, mean effect and response surface methodology (RSM). The tool rotational speed was verified to be the most influent factor in the joint mechanical properties, and is strongly dependent on the shoulder/probe diameters ratio. It was also shown that using 1000 rpm, 3.90 mm of probe depth and shoulder/probe diameters ratio of 2.5 (shoulder diameter of 15 mm) it may be achieved improved joint strength. For the optimized parameters it was verified that the welding speed does not have a significant influence. Equations to predict the joints mechanical properties were also derived through multiple regression.
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