Identifying friction stir welding process parameters through coupled numerical and experimental analysis

Zhou, Xin; Pan, Wenke; Mackenzie, Donald

doi:10.1016/j.ijpvp.2013.04.001

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…In recent years, many studies were carried out to optimize the FSW process parameters by various methods. 16–19 Zhou et al 20 investigated the FSW parameters by numerical and experimental methods. They applied finite element model for the thermal analysis of FSW process.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and optimization of friction stir welding process parameters in AA5083 and AA7075 aluminum alloy joints

Saeidi

Manafi

Givi

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this study, dissimilar joints of AA5083-H116 and AA7075-T6 aluminum alloys were successfully made by friction stir welding technique. The microstructure and mechanical behavior of the welded joints were investigated at different rotational and traverse speeds. A mathematical modeling was developed to demonstrate a relationship between the friction stir welding parameters and the ultimate tensile strength of the dissimilar joints. Then, the mathematical modeling was optimized by genetic algorithm in order to find the optimum condition in which the maximum tensile strength of welded joints can be achieved. Eventually, genetic algorithm results confirmed that the maximum tensile strength of welded joints is achievable in rotational and traverse speeds of 500 r/min and 50 mm/min, respectively. The maximum error between experimental data and predicted model was less than 1%.

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Section: Introductionmentioning

confidence: 99%

Mathematical modeling and optimization of friction stir welding process parameters in AA5083 and AA7075 aluminum alloy joints

Saeidi

Manafi

Givi

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Since material flow governs microstructure changes, an attempt was made to develop detailed thermal-metallurgical-mechanical model (Feng et al, 2007) to study the microstructure changes and their effects on residual stress and distortion (Feng et al, 2007;Zhou et al, 2013). Feng et al (2007) used a three dimensional FE model that incorporated changes in the strength of AA6061-T6 due to dissolution and reformation of precipitates.…”

Section: Thermo-mechanical Modellingmentioning

confidence: 99%

“…Feng et al (2007) used a three dimensional FE model that incorporated changes in the strength of AA6061-T6 due to dissolution and reformation of precipitates. Zhou et al (2013) coupled FE method with a genetic algorithm with an aim to optimise the values of thermal inputs and later use them for residual stress prediction. Attempts were also made to study the effect of pin design on thermomechanics of metal flow in order to improve joint strength (Buffa et al, 2006b) and process design (Buffa et al, 2009).…”

Section: Thermo-mechanical Modellingmentioning

confidence: 99%

“…Hence it was stated that the FE model can be used to predict other parameters of the FSW process in future studies. Zhou et al (2013) presented a simplified FE model for analysis of FSW. Thermal progress was proposed in which two parameters, tool heat input rate and heat loss through the backing plate were identified for optimisation through the application of a genetic algorithm.…”

Section: Thermo-mechanical Modellingmentioning

confidence: 99%

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Review on modelling of friction stir welding using finite element approach and significance of formulations in simulation

Malik¹,

Sanjeev²,

Bajakke³

2020

IJMR

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Friction stir welding (FSW) is a solid-state joining process, which is gaining significance in many joining applications, by overcoming the limitations of other fusion welding processes. For successful incorporation of its potential during industrial applications, mechanism of joining needs to be properly comprehended. The solution lies in developing effective and reliable finite element (FE) model of the FSW process, which would help in getting an insight of the process phenomena (like material flow, heat generation, etc.) during the process. The overall result could be used to observe the effect of process parameters on weld quality. Several attempts have been made to develop an FE model for FSW using different techniques. However, building an efficient model that emulates reality is still to be realised. Here, a review is made to know the current state of various FE modelling techniques and identifying better techniques for simulating FSW and its variants. This review also highlights shortcomings (for ex: mesh distortion, simulation time, the capability of defect prediction) of previous models and discusses on grey areas which are still to be addressed in the broader perspective of FSW and its allied processes using FE approach.

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“…Tutum and Hattel (2010) minimized residual stress and maximized the traverse speed of friction stir welding process using a genetic algorithm. Zhou et al (2013) optimized the heat input and heat loss rates of backing plate for FSW using a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Tensile Properties Improvement on Friction Stir Welded Age-Hardenable Aluminum Alloys: An Evolutionary Approach using RSM based GA and SA

2016

Rev. Téc. Ing. Univ. Zulia.

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AA2024 and AA6061 aluminum alloys were fabricated using the friction stir welding technique to improve the tensile properties such as ultimate tensile strength (UTS) and tensile elongation (TE). The BoxBehnken design matrix with four significant factors-rotational speed (X(1)), welding speed (X(2)), axial load (X(3)), and pin shape (X(4)), each having three levels-was used with response surface methodology to develop a mathematical model. The experiments yielded ultimate tensile strength of 141 MPa at 12% tensile elongation. The obtained tensile properties of fabricated weld joints were low when compared to those of base materials. Several voids and pinholes were found, these being the consequence of poor tensile properties on the fabricated weld joints. Therefore, to maximize the tensile properties, two computational approaches were employed: a genetic algorithm and a simulated annealing algorithm. The main objectives of applying these computational approaches were: (1) to maximize the UTS and TE compared to the welding performance value of the experimental data and regression modeling; (2) to estimate the optimal process parameter values that must be within the obtained range of the minimum and maximum values; and (3) to evaluate the number of iterations generated by the computational approaches. The results show that use of the computational approaches resulted in significant improvement in the tensile properties. Comparing the genetic algorithm with the simulated annealing algorithm, the latter maximizes the tensile properties of fabricated AA2024 and AA6061 aluminum alloy weld joints; however, a considerable number of iterations and substantial amount of time was required to achieve a global optimal solution.

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Identifying friction stir welding process parameters through coupled numerical and experimental analysis

Cited by 18 publications

References 6 publications

Mathematical modeling and optimization of friction stir welding process parameters in AA5083 and AA7075 aluminum alloy joints

Mathematical modeling and optimization of friction stir welding process parameters in AA5083 and AA7075 aluminum alloy joints

Review on modelling of friction stir welding using finite element approach and significance of formulations in simulation

Tensile Properties Improvement on Friction Stir Welded Age-Hardenable Aluminum Alloys: An Evolutionary Approach using RSM based GA and SA

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