Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization

Abstract

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.