Prediction of optimum welding parameters for FSW of aluminium alloys AA6063 and A319 using RSM and ANN

Krishnan, M. Muthu; Maniraj, J.; Deepak, R.; Anganan, K.

doi:10.1016/j.matpr.2017.11.138

Cited by 42 publications

(19 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where Q slide is the generated heat while sliding (watts), µ is the friction coefficient of the material, p is the axial force (kN), is the tool angular rotation speed (rad −1 ), D is the diameter of the shoulder (mm), d is the diameter of the pin (mm), and α is the shoulder angle ( • ). Table 1 lists the welding parameters and their values gathered from the literature review [8,9,[13][14][15][16][17][18][19]. The table shows that three factors affect the tensile efficiency: rotational speed (rpm), welding speed (mm/min), and axial force (kN).…”

Section: Introductionmentioning

confidence: 99%

Combined Response Surface Method and Modified Differential Evolution for Parameter Optimization of Friction Stir Welding

et al. 2020

View full text Add to dashboard Cite

In this study, we constructed a new algorithm to determine the optimal parameters for friction stir welding including rotational speed, welding speed, axial force, tool pin profile, and tool material. The objective of welding is to maximize the ultimate tensile strength of the welded aluminum. The proposed method combines the response surface method and the modified differential evolution algorithm (RSM-MDE). RSM-MDE is a method that involves both experimental and simulation procedures. It is composed of four steps: (1) finding the number of parameters and their levels that affect the efficiency of the friction stir welding, (2) using RSM to formulate the regression model, (3) using the MDE algorithm to find the optimal parameter of the regression model obtained from (2), and (4) verifying the results obtained from step (3). The optimal parameters generated by the RSM-MDE method were a rotation speed of 1417.68 rpm, a welding speed of 60.21 mm/min, an axial force of 8.44 kN, a hexagon-tapered tool pin profile, and the SKD 11 tool material. The ultimate tensile strength obtained from this set of parameters was 294.84 MPa, which was better than that of the RSM by 1.48%.

show abstract

Section: Introductionmentioning

confidence: 99%

Combined Response Surface Method and Modified Differential Evolution for Parameter Optimization of Friction Stir Welding

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Some finite element models (FEM) are applicable for numerical modelling and a physical understanding of the different parameters in FSW [22,23] and also for simulating the correlation between these parameters where complex boundary conditions implemented by artificial neural network (ANN) models are available in the literature [24,25]. Simulating FSW is a complex issue, combining many scientific disciplines [22].…”

Section: Introductionmentioning

confidence: 99%

An advanced modelling to improve the prediction of thermal distribution in friction stir welding (FSW) for difficult to weld materials

Sarikavak

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

This study explores the thermal distribution of high-strength engineering alloys during the friction stir welding process (FSW). Materials which are difficult to weld or are unweldable by conventional welding processes can be successfully welded by FSW. The specific analysis and modification of the process require an understanding of the actual mechanism of the process. Therefore, a transient, three-dimensional, thermo-mechanical finite element model (FEM) for FSW was developed. The model calculates the temperature distribution during the welding process considering various boundary conditions such as rotational speed, linear speed, normal pressure, tool diameter and material properties. The thermo-mechanical FEM calculations consider the effects of conduction and convection heat transfer. The numerical results are successfully compared and validated by experimental results published in the literature for aluminium alloy, titanium alloy and steel (mild and bainitic) as workpiece materials. The model was found to be useful for understanding the effects of changes in different system parameters, and for selecting the optimum welding conditions before undertaking high-cost physical testing.

show abstract

“…ANN is already known as 'universal function approximator' [15] for its ability to model underlying function in a dataset to any arbitrary degree of accuracy. It has been employed for process modelling in electric discharge machining [16], laser cutting [17,18], laser welding [19] and conventional welding [20]. But all research work mentioned above has employed single hidden layer back-propagation neural network (BPNN) technique [21] for modelling.…”

Section: Introductionmentioning

confidence: 99%

Neural networks based prediction modelling of hybrid laser beam welding process parameters with sensitivity analysis

Chaki¹

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Present paper attempted to model complex relationship between CO 2 laser-MIG hybrid welding parameters and it has been completed using different algorithms of artificial neural networks (ANN). Input parameters for the study include laser power, welding speeds and wires feed rate and tensile strength of the joint is considered as output. A full factorial experimental dataset is used for the purpose. Variants of back propagation neural networks (BPNN) and Radial Basis Function Networks have been used as training algorithm. Altogether 65 different ANN architecture have been trained and tested using 6 different training algorithms to find out ANN with best prediction capability. 3-11-1 ANN architecture trained using BPNN with Bayesian regularization shows best prediction capability (mean square error 3.24E − 04) and considered as Best ANN. That ANN will be useful for determining required value of welding process parameters to yield a specific welding strength and suitable for online process monitoring and control. Finally, a sensitivity analysis has been conducted and it is found that, maximum welding strength can be obtained with low wire feed rate (4 m/min), low welding speed (2 m/min) and high laser power (3 kW).

show abstract

Prediction of optimum welding parameters for FSW of aluminium alloys AA6063 and A319 using RSM and ANN

Cited by 42 publications

References 4 publications

Combined Response Surface Method and Modified Differential Evolution for Parameter Optimization of Friction Stir Welding

Combined Response Surface Method and Modified Differential Evolution for Parameter Optimization of Friction Stir Welding

An advanced modelling to improve the prediction of thermal distribution in friction stir welding (FSW) for difficult to weld materials

Neural networks based prediction modelling of hybrid laser beam welding process parameters with sensitivity analysis

Contact Info

Product

Resources

About