Investigation and multi-objective optimization of friction stir welding of AA7075-T651 plates

Abstract

The present study investigates and comparatively evaluates different evolutionary algorithms for multi-objective optimization of friction stir welding (FSW) of AA7075-T651 plates. The FSW parameters are optimized for obtaining the joint with higher tensile strength, microhardness and lower surface roughness using particle swarm optimization, strength Pareto-based evolutionary algorithm II, differential evolution and teaching learning-based optimization techniques. The validation experiments showed better prediction accuracy with the SPEA II technique. This study finds maximum tensile strength and microhardness of 187.45 MPa and 142.47 HV, respectively, and minimum surface roughness of 15.93 µm for FSW joint when using a welding speed and tool rotation, of 40 mm/min and 1923 rpm, respectively. The FSW joint obtained at these optimized parameters showed the homogeneous material mixing with the equiaxed fine-grain distribution at the weld nugget with fewer voids, as confirmed by the scanning electron microscopic images with energy-dispersive X-ray spectroscopic analysis.

Keywords:

• Friction stir welding
• evolutionary algorithms
• PSO
• TLBO
• SPEA II

Disclosure statement

No potential conflict of interest was reported by the author(s).