scholarly journals Genetic Algorithm-Based Optimization of Friction Stir Welding Process Parameters on Aa7108

2021 ◽  
Vol 10 (8) ◽  
pp. 47-53
Author(s):  
Maulikkumar B. Patel ◽  
Komal G. Dave
Keyword(s):  
Genetic Algorithm ◽  
Tensile Strength ◽  
Friction Stir Welding ◽  
Process Parameters ◽  
Fitness Function ◽  
Welding Process ◽  
Friction Stir ◽  
Friction Stir Welding Process ◽  
Confirmation Test ◽  
Microstructure Examination

This research paper deals with the characterization of friction stir welding aluminium 7108 with twin stir technology. The coupons of the above metal were friction stir welded using a cylindrical pin with counter-rotating twin stir technology using at constant speed 900, 1200, 1500,1800 with four different feed rates of 30,50,70,90 mm/min. Microstructure examination showed the variation of each zone and their influence on the mechanical properties. Also, tensile strength and hardness measurements were done as a part of the mechanical characterization and correlation between mechanical and metallurgical properties and deduced at the speed of 1500 rpm. Friction stir welding process parameters such as tool rotational speed (rpm), tool feed (mm/min) were considered to find their influence on the tensile strength (MPa) and hardness (HRB). A genetic algorithm (GA) was employed by taking the fitness function as a combined objective function to optimize the friction welding process parameters to predict the maximum value of the tensile strength and hardness. The confirmation test also revealed good closeness to the genetic algorithm predicted results and the optimized value of process parameters for different weights of the tensile and hardness have been predicted in the model.

Prediction and optimization of the mechanical properties of dissimilar friction stir welding of aluminum alloys using design of experiments

2016 ◽  
Vol 232 (8) ◽  
pp. 1384-1394 ◽  
Author(s):  
R Palanivel ◽  
RF Laubscher ◽  
S Vigneshwaran ◽  
I Dinaharan
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Friction Stir Welding ◽  
Aluminum Alloys ◽  
Ultimate Tensile Strength ◽  
Design Of Experiments ◽  
Process Parameters ◽  
Welding Process ◽  
Friction Stir ◽  
Friction Stir Welding Process

Friction stir welding is a solid-state welding technique for joining metals such as aluminum alloys quickly and reliably. This article presents a design of experiments approach (central composite face–centered factorial design) for predicting and optimizing the process parameters of dissimilar friction stir welded AA6351–AA5083. Three weld parameters that influence weld quality were considered, namely, tool shoulder profile (flat grooved, partial impeller and full impeller), rotational speed and welding speed. Experimental results detailing the variation of the ultimate tensile strength as a function of the friction stir welding process parameters are presented and analyzed. An empirical model that relates the friction stir welding process parameters and the ultimate tensile strength was obtained by utilizing a design of experiments technique. The models developed were validated by an analysis of variance. In general, the full impeller shoulder profile displayed the best mechanical properties when compared to the other profiles. Electron backscatter diffraction maps were used to correlate the metallurgical properties of the dissimilar joints with the joint mechanical properties as obtained experimentally and subsequently modeled. The optimal friction stir welding process parameters, to maximize ultimate tensile strength, are identified and reported.

Multi-response optimization of friction stir welding process parameters for dissimilar joining of Al6101 to pure copper using standard deviation based TOPSIS method

2019 ◽  
Vol 233 (18) ◽  
pp. 6473-6482 ◽  
Author(s):  
Nidhi Sharma ◽  
Zahid A Khan ◽  
Arshad Noor Siddiquee ◽  
Mohd Atif Wahid
Keyword(s):  
Tensile Strength ◽  
Friction Stir Welding ◽  
Ultimate Tensile Strength ◽  
Process Parameters ◽  
Welding Speed ◽  
Welding Process ◽  
Micro Hardness ◽  
Friction Stir ◽  
Friction Stir Welding Process ◽  
Response Optimization

Friction stir welding is a new and effective solid-state welding process for joining dissimilar materials such as aluminum (Al) and copper (Cu). Joint quality of the friction stir welded materials gets influenced by the welding strategy and different friction stir welding process parameters, i.e. rotational speed, welding speed, tool design, tool pin offset, and tilt angle. In this paper, the effect of combination of different friction stir welding process parameters during joining of Al-6101 and pure copper is studied using Taguchi L18 orthogonal array. Four friction stir welding process parameters, i.e. shoulder diameter (A), pin offset (B), welding speed (C), and rotational speed (D) each at three levels except shoulder diameter, which is at two levels are selected. The effect of different combinations of these parameters on ultimate tensile strength and micro-hardness of the joints is investigated. Subsequently, single response optimization for ultimate tensile strength and micro-hardness and multi-response optimization of ultimate tensile strength and micro-hardness taken together is carried out to obtain the optimal combination of the friction stir welding process parameters. Taguchi method is used for single response optimization, whereas Taguchi-based TOPSIS method is employed for multi-response optimization. For single optimization, the optimum combination of the friction stir welding parameters yielding maximum strength and micro-hardness are A1B1C2D2 and A2B1C2D3, respectively. The optimum combination of the process parameters for multi-response optimization is A2B1C2D2. From the results of the study for single- and multi-response optimization, it is revealed that the rotational speed is the most significant process parameter affecting the tensile strength and micro-hardness of the joints followed by the welding speed. Further, the macro/microstructure and micro-hardness profile of the joint obtained at the optimal combination of the multi-response optimization are given and discussed for better understanding of material mixing and joining.

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