Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a...

Area of Science:

• Materials Science
• Mechanical Engineering
• Manufacturing Processes

Background:

• Friction Stir Welding (FSW) offers a high-quality, rapid, and reliable joining method for metals.
• Dissimilar aluminum alloy welding presents challenges in achieving optimal mechanical properties.
• Understanding the influence of FSW parameters on weld characteristics is crucial for industrial applications.

Purpose of the Study:

• To investigate the mechanical properties of dissimilar Friction Stir Welded joints between AA6061-T6 and AA7075-T6 aluminum alloys.
• To develop a mathematical regression model correlating FSW process parameters with mechanical properties.
• To employ a multi-objective optimization algorithm for simultaneously enhancing ultimate tensile strength, elongation, and minimizing hardness in the heat-affected zone (HAZ).

Main Methods:

• Utilized Friction Stir Welding (FSW) for joining AA6061-T6 and AA7075-T6 aluminum alloys.
• Developed a mathematical regression model to establish empirical relationships between FSW parameters (rotational speed, traverse speed, tilt angle, tool offset) and mechanical properties.
• Applied a biogeography-based optimization algorithm for multi-objective optimization of FSW parameters.
• Employed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Shannon's entropy for decision-making to select the optimal solution.

Main Results:

• Established validated empirical relationships between FSW process parameters and the mechanical properties of dissimilar aluminum alloy joints.
• Generated Pareto optimal frontiers for optimizing ultimate tensile strength, elongation, and heat-affected zone (HAZ) hardness.
• Identified optimal FSW process parameters that simultaneously enhance key mechanical properties.

Conclusions:

• The study successfully optimized FSW parameters for dissimilar AA6061-T6/AA7075-T6 aluminum alloys.
• The developed regression model and multi-objective optimization approach provide a framework for improving weld quality.
• The findings contribute to advancing FSW technology for manufacturing high-performance dissimilar metal joints.