Extended hybrid statistical tools ANFIS- GA to optimize underwater friction stir welding process parameters for ultimate tensile strength amelioration

2021 ◽  
Author(s):  
Ibrahim Sabry Given Name ◽  
Noah E. El-Zathry ◽  
F.T. El-Bahrawy Given ◽  
M. Abdel Ghaffar
Keyword(s):  
Tensile Strength ◽  
Friction Stir Welding ◽  
Ultimate Tensile Strength ◽  
Process Parameters ◽  
Welding Process ◽  
Statistical Tools ◽  
Friction Stir ◽  
Friction Stir Welding Process ◽  
Underwater Friction Stir Welding

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.

scholarly journals Effect of process parameters on mechanical properties of AA5052 joints using underwater friction stir welding

2020 ◽  
Vol 14 (1) ◽  
pp. 6259-6271
Author(s):  
Srinivasa Rao Pedapati ◽  
Dhanish Paramaguru ◽  
Mokhtar Awang ◽  
Hamed Mohebbi ◽  
Sharma V Korada
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Friction Stir Welding ◽  
Process Parameters ◽  
Welding Process ◽  
Welding Parameters ◽  
Fracture Characterization ◽  
Friction Stir ◽  
Weld Joints ◽  
Underwater Friction Stir Welding

Underwater Friction Stir Welding (UFSW) is a solid-state joining technique which uses a non-consumable tool to weld metals. The objective of this investigation is to evaluate the mechanical properties of the AA5052 Aluminium alloy joints prepared by UFSW. The effect of different type of welding tools and welding parameters on the weld joint properties are studied. Square, tapered cylindrical and taper threaded cylindrical type of welding tools have been used to produce the joints with the tool rotational speed varying from 500 rpm to 2000 rpm while the welding speed varying from 50 mm/min to 150 mm/min. Tensile strength, micro-hardness distribution, fracture features, micro-and macrostructure of the fabricated weld joints have been evaluated. The effect of welding process parameters that influences the mechanical properties and fracture characterization of the joints are explained in detail. A maximum Ultimate Tensile Strength (UTS) value of 222.07 MPa is attained with a gauge elongation of 14.78%. Microstructural evaluation revealed that most of the fracture are found on the thermal mechanically affected zone (TMAZ)adjacent to the weld nugget zone (WNZ) due to bigger grain sizes. It is found that most of the joints exhibit ductile characteristics in failure. Fractography analysis has been used to find the behavior of weld joints in failure.

A simulation-based study on the effect of underwater friction stir welding process parameters using different evolutionary optimization algorithms

2019 ◽  
Vol 234 (2) ◽  
pp. 643-657 ◽  
Author(s):  
Mohd A Wahid ◽  
Sarfaraz Masood ◽  
Zahid A Khan ◽  
Arshad N Siddiquee ◽  
Irfan A Badruddin ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Friction Stir Welding ◽  
Welding Process ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Friction Stir ◽  
Friction Stir Welding Process ◽  
Underwater Friction Stir Welding ◽  
Evolutionary Optimization Algorithms ◽  
Firefly Optimization

This paper investigates the effect of underwater friction stir welding process parameters on the mechanical properties of the aluminum alloy 6082-T6 joint and further simulates this process using various evolutionary optimization algorithms. Three independent underwater friction stir welding process parameters, i.e. shoulder diameter (in mm) at two levels, rotational speed (in r/min) at three levels, and traverse speed (in mm/min) also at three levels, were varied according to the Taguchi’s L18 standard orthogonal array. The effect of variations in these parameters, on the ultimate tensile strength (in MPa), percentage elongation (in %), and impact strength (in J) of the welded joint was experimentally measured and recorded. In order to simulate this underwater friction stir welding process, three evolutionary optimization algorithms, i.e. particle swarm optimization, firefly optimization, and non-dominated sorting established on the genetic algorithm (NSGA-II), were employed. In these simulations, an artificial neural network with two layers, resembling a non-linear function, was employed as the cost function to predict the values of the response variables, i.e. ultimate tensile strength, elongation, and impact strength, which were experimentally measured earlier. In these simulations, several experiments were conducted using different randomly selected data set and subsequently, the accuracy of each individual simulation was compared. Results revealed that the firefly optimization-based simulation performed the best with least mean squared error while predicting the response variable values, as compared to the particle swarm optimization and the NSGA-II. The minimum value of the mean squared error for the firefly optimization-based simulation was observed to be as low as 0.009%, 0.004%, and 0.017% for ultimate tensile strength, elongation and impact strength, respectively. Furthermore, it was also observed that the computational time for the firefly optimization-based simulation was significantly lower than that of both particle swarm optimization and NSGA-II-based simulations.

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.

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