scholarly journals OPTIMIZATION OF DISSIMILAR FRICTION STIR WELDED ALUMINUM PATES (2024 T3 AND 7075T6) BY USING DIFFERENT METHODS

2021 ◽  
Author(s):  
Rasha M . Hussien
Keyword(s):  
Particle Swarm Optimization ◽  
Friction Stir Welding ◽  
Particle Swarm ◽  
Welding Process ◽  
Fusion Welding ◽  
Maximum Efficiency ◽  
Swarm Optimization ◽  
Rotating Speed ◽  
Friction Stir ◽  
Modified Particle Swarm Optimization

Friction stir welding (FSW) has many advantages when compared with another fusion welding. The experimental analysis and optimization of friction stir welding (FSW) were done to obtain desired mechanical properties of dissimilar aluminum welded plates (2024T3 and 7075T6). The friction stir welding process was done on aluminum plates (2024T3 and 7075T6) for different three rotating speeds (710, 1120 and 1800), three welding speeds (25, 50 and 77), three different steel tools (Square, cylindrical and Hexagonal) and 2° title angle. The different tests of welding were done according to the orthogonal matrix of experimental design analysis, then a tensile test was done to calculate the ultimate stress to get the welding efficiency. The optimum welding environment led to the maximum efficiency was obtained by these methods (Taguchi, Particle Swarm Optimization and new modified Particle Swarm Optimization). Particle swarm optimization (and its new modification) used an artificial neural network to find the relation between the input and output parameters. The results showed that when the rotating speed is increased and welding speed is decreased (but this conclusion depends on tool shape) the welding efficiency is increased. The present study showed that the modified PSO is the best method to find the optimum welding environment as compared with experimental results

Tensile Properties and Microstructure of Friction Stir Welded Joints 2195-T8 Aluminum Alloy

2016 ◽  
Vol 835 ◽  
pp. 191-196 ◽  
Author(s):  
Kookil No ◽  
Ye Rim Lee ◽  
Jong Hoon Yoon ◽  
Joon Tae Yoo ◽  
Ho Sung Lee
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Aluminum Alloys ◽  
Welding Process ◽  
Tensile Tests ◽  
Fusion Welding ◽  
Homogeneous Microstructure ◽  
Rotating Speed ◽  
Friction Stir ◽  
Reactive Property

Friction stir welding is a widely used welding process for aluminum alloys because it avoids many of the problems of conventional fusion welding. This process is beneficial especially for lithium containing aluminum alloys in which the reactive property of element Li causes porosity and hot cracking during melting and solidification. In friction stir welding process, each region undergoes different thermo-mechanical cycles and produces a non-homogeneous microstructure. In the present study, the mechanical properties and microstructure of a 2195-T8 aluminum alloy joined with friction stir welding were investigated. The change in microstructure across the welded joint was found to correspond to microhardness measurement. The microstructure was characterized by the presence of severely deformed grains and fine recrystallized grains depending on the region. Tensile tests shows the optimum condition was obtained at the tool rotating speed of 600rpm and the traveling speed range from 180 to 300mm/min.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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