What is it about?
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.
Photo by Kiefer Likens on Unsplash
Why is it important?
The input parameters were optimized using teaching–learning-based optimization algorithm technique to improve mechanical properties.
Read the Original
This page is a summary of: Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization, Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, December 2019, SAGE Publications, DOI: 10.1177/0954406219891755.
You can read the full text:
The following have contributed to this page