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Realizing a Novel Friction Stir Processing-Enabled FWTPET Process for Strength Enhancement Using Firefly and PSO
Senthil Kumaran S1, Jayakumar Kaliappan2, Kathiravan Srinivasan3
1Department of Manufacturing Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
Materials (Basel, Switzerland)
|February 9, 2020
Summary
This study enhanced friction stir welding of aluminum alloys by adding carbon nanotubes and silicon nitride particles. The firefly algorithm optimized parameters, achieving superior tensile strength predictions compared to particle swarm optimization.
Area of Science:
- Materials Science and Engineering
- Manufacturing Processes
- Nanotechnology
Background:
- Friction stir welding of tube to tube plate (FWTPET) is crucial in aerospace, automotive, and power industries.
- Enhancing weld zone properties like tensile strength and hardness is vital for component integrity.
- Incorporating nanoparticles into friction stir processing (FSP) offers a route to improved mechanical characteristics.
Purpose of the Study:
- To investigate the combined Friction Stir Welding of Tube to Tube Plate External Tool (FWTPET) and Friction Stir Processing (FSP) for joining aluminum alloys.
- To reinforce the weld zone of AA6063 tubes to AA6061 tube plates using carbon nanotubes (CNT) and silicon nitride (Si3N4) particles.
- To optimize FWTPET + FSP process parameters using computational algorithms and compare their predictive accuracy for tensile strength.
Main Methods:
- The study employed FWTPET for joining AA6063 tubes to AA6061 tube plates, followed by FSP for nanoparticle reinforcement.
- Carbon nanotubes (CNT) and silicon nitride (Si3N4) particles were utilized to enhance the weld zone.
- Taguchi L25 orthogonal array was used for parameter identification, while Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA) were used for parameter optimization.
Main Results:
- Both PSO and FFA were employed to determine optimal input parameters (CNT, Si3N4, rotational speed, depth) and predict output tensile strength.
- Experimental validation showed minimal deviation between predicted and actual tensile strength values obtained through PSO and FFA.
- The Firefly Algorithm (FFA) demonstrated superior accuracy in predicting the tensile strength of the reinforced welded joints compared to the Particle Swarm Optimization (PSO) method.
Conclusions:
- The integrated FWTPET + FSP approach, enhanced with CNT and Si3N4, effectively improves the mechanical properties of aluminum alloy joints.
- Computational optimization algorithms like FFA and PSO are valuable tools for predicting and refining welding process parameters.
- FFA offers a more precise predictive capability for tensile strength in nanoparticle-reinforced friction stir welded joints, guiding future material design and manufacturing.
