Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

This paper explicates the joining of AA 6061/TiO 2 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 to...

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Bibliographic Details
Published in:Welding in the world 2022, Vol.66 (1), p.93-103
Main Authors: Prabhu, Subramanya R., Shettigar, Arun, Herbert, Mervin A., Rao, Shrikantha S.
Format: Article
Language:English
Subjects:
Algorithms
Aluminum base alloys
Chemistry and Materials Science
Composite materials
Friction stir welding
Hardness
Machine learning
Materials Science
Mechanical properties
Metallic Materials
Optimization
Process parameters
Research Paper
Response surface methodology
Solid Mechanics
Theoretical and Applied Mechanics
Titanium dioxide
Ultimate tensile strength
Variance analysis
Welding parameters
Yield strength
Yield stress
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Summary:This paper explicates the joining of AA 6061/TiO 2 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.
ISSN:0043-2288
1878-6669
DOI:10.1007/s40194-021-01187-z