Abstract
This paper explicates the joining of AA 6061/TiO2 composites by the friction stir welding (FSW) process. FSW experiments were conducted as per the three factors, three-level, central composite ivy– face-centered design method. Mathematical relationships between the FSW process parameters, namely tool geometry, welding speed, and tool rotational speed, and the output responses such as hardness, yield strength, and ultimate tensile strength were established using response surface methodology. Adequacies of established models were assessed through the analysis of variance method. Further, the paper elucidates the application of the teaching–learning-based optimization (TLBO) algorithm to identify the optimal values of input variables and to obtain an FSW joint with superior mechanical properties. The optimized experimental condition obtained from the TLBO yields an FSW joint with a UTS of 174 MPa, yield strength of 120 MPa, and hardness of 126HV. The study revealed that the result of the TLBO algorithm matched the findings of the FSW experiments.
Highlights
As a solid-state thermo-mechanical joining technique, friction stir welding (FSW) is considered a promising method for welding aluminum matrix composites (AMCs) as it has the ability to eliminate the defects such as voids, porosity, and cracks associated with the traditional metal welding process [1]
The effect of FSW process variables and their interactions was determined in TiO2-reinforced aluminum matrix composite FSW joints, and the mathematical relationships were established for output responses in terms of independent input process variables
Models developed for yield strength (YS), ultimate tensile strength (UTS), and hardness are satisfactory as the measured F ratios are lesser than the computed results at a 95% level of confidence; the values, R2, and adjusted R2 indicate that the models are passable enough for further analysis
Summary
As a solid-state thermo-mechanical joining technique, friction stir welding (FSW) is considered a promising method for welding aluminum matrix composites (AMCs) as it has the ability to eliminate the defects such as voids, porosity, and cracks associated with the traditional metal welding process [1]. It is learned that several traditional methods were used for the optimization of the FSW process, but these methods do not work well over a wider range of problems, and often, they offer a local optimum solution An evolutionary algorithm such as GA can overcome these limitations, but efficient usage of this technique depends on the size of the population and the diversity of each solution in the given problem. Firstly the detail of experiments conducted to join AA6061/TiO2 composite by FSW process is explained, followed by the steps to develop a mathematical model between the input parameters and the output responses using RSM that is illustrated. The comprehensive explanation on working and the application of the TLBO algorithm on the developed models are shown along with the confirmation trials, conducted to confirm the relevance of the algorithm for the present FSW process
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