Microstructure, tensile and fractographic behavior of friction stir welded joints AA6061 and AA2024
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https://orcid.org/0009-0003-8578-312X
N. Nagesha
Department of Industrial and Production Engineering, University B. D. T. College of Engineering, Davangere, Visvesvaraya Technological University, Belagavi, Karnataka India.
https://orcid.org/0000-0001-9088-1734
Manjunath Vatnalmath
Department of Mechanical Engineering, RNS Institute of Technology, Bangalore-566098, Visvesvaraya Technological University, Belagavi, Karnataka, India.
https://orcid.org/0000-0003-3138-9453
Mr SathyaNarayana N
Department of Mechanical Engineering, Government Engineering college, Mosalehosalli – 573212, India
https://orcid.org/0009-0001-5754-2332
Abstract
The present study focused on the evaluation of critical influencing parameters of the FSW of AA6061-T6 and AA2024-T351 alloys. The materials properties of AA6061-T6 are heat-treatable alloys possessing properties of good resistance to corrosion, and AA 2024-T351 has superior strength, which is very important in load bearing section. The joining of these AA alloys will result in excellent mechanical qualities. Friction stir welding (FSW) is a very crucial joining method in the aerospace and automobile sector resolving most of the problems connected to the requirement of high-performance welded joints. In preference, this kind of joining has more advantages over common joining processes, such as less defects, no use of consumable electrodes, and it can be applicable in welding at any position. In this research, optimization of critical influencing input parameters was conducted using the L9 orthogonal array by the Taguchi method. Overall, there were 9 experimental trials after designing in the statistical software. The input process parameters selected for optimization are tool rotation speed (TRS), welding feed rate (WS) and tilt angle, which is maintained constant at zero degrees. The relative contribution percentages of the input variables to the optimized outputs (TRS and WS) were determined via ANOVA.
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