Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization
| Autor: | K Venkata Rao, L. Suvarna Raju, Borigorla Venu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Mathematical model Computer science Mechanical Engineering Process (computing) Mechanical engineering Rotational speed 02 engineering and technology 021001 nanoscience & nanotechnology Multi-objective optimization 020901 industrial engineering & automation Friction stir welding 0210 nano-technology Teaching learning |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 234:1146-1155 |
| ISSN: | 2041-2983 0954-4062 |
| DOI: | 10.1177/0954406219891755 |
| Popis: | This study focuses on optimization of process parameters, which may result in improved mechanical properties of the friction stir weldments of AA2014-T651. Plain taper and threaded taper cylindrical tool pin profiles were used for the study. A set of experiments was conducted at different levels of tool rotational and weld speeds using two tool pin profiles. Mechanical properties such as tensile strength, yield strength, impact strength, percentage of elongation, and hardness were measured. Objective functions are developed for the five mechanical properties in terms of input parameters. The input parameters were optimized using teaching–learning-based optimization algorithm technique to improve mechanical properties. The teaching–learning-based optimization algorithm suggested three best combinations such as combination-I (940 r/min and 32 mm/min), combination-II (1100 r/min and 40 mm/min), and combination-III (1205 r/min and 45 mm/min). The optimization is also validated with experimental results. |
| Databáze: | OpenAIRE |
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