Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization

Venu, Borigorla; Raju, L. Suvarna; Rao, K. Venkata

doi:10.1177/0954406219891755

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…MATLAB R2019b software was used to develop the TLBO algorithm. TLBO requires the only size of the population and the total number of iterations to develop the algorithm [40]. The size of the population for the current study was fixed as 20 and the number of iteration as 50.…”

Section: Learner Levelmentioning

confidence: 99%

Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

et al. 2021

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This paper explicates the joining of AA 6061/TiO2 composites by the friction stir welding (FSW) process. FSW experiments were conducted as per the three factors, three-level, central composite ivy– face-centered design method. Mathematical relationships between the FSW process parameters, namely tool geometry, welding speed, and tool rotational speed, and the output responses such as hardness, yield strength, and ultimate tensile strength were established using response surface methodology. Adequacies of established models were assessed through the analysis of variance method. Further, the paper elucidates the application of the teaching–learning-based optimization (TLBO) algorithm to identify the optimal values of input variables and to obtain an FSW joint with superior mechanical properties. The optimized experimental condition obtained from the TLBO yields an FSW joint with a UTS of 174 MPa, yield strength of 120 MPa, and hardness of 126HV. The study revealed that the result of the TLBO algorithm matched the findings of the FSW experiments.

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Section: Learner Levelmentioning

confidence: 99%

Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

et al. 2021

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“…The TLBO algorithm is an algorithm which is inspired from teaching and learning capability of teacher and learner and knowledge is flows from teacher to learner to recognize all the ideas and implement best idea either from teacher or learner or from both to obtain optimum solution. 21,23 In this algorithm, a population is taken which is considered as group of learners and subjects given to the learners are taken as different design variables which is actually input processing parameters for the optimization problem. The best solution obtained from whole population is assumed as the teacher and this solution is also representing the optimum solution of objective function.…”

Section: Tlbo Algorithmmentioning

confidence: 99%

“…In their experiments, they found that TRS was the most influencing factor for TS while axial force does not significantly affect the TS. Venu et al 23 observed the mechanical properties of FSWed AA2014-T651 by plain and threaded tapered cylindrical pin tool using L9 orthogonal array design matrix. TRS and WS as input parameters and teaching-learning-based optimization (TLBO) algorithm as optimizing technique was selected to improve the joint properties.…”

Section: Introductionmentioning

confidence: 99%

Study of optimum welding performance in friction stir welding of dissimilar Mg alloys using integrated RSM-TLBO algorithm

Singh

Dubey

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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Lightweight with excellent strength of magnesium alloys has attracted its use in transportation industries but difficulty in fusion welding of magnesium alloys restricts its application. The present research investigates solid state friction stir welding of dissimilar AZ31-AZ91 magnesium alloys with aim to achieve optimum quality welds. Surface roughness, microstructure and mechanical properties of these joints have been investigated at different tool rotational speed, welding speed and tool shoulder diameter. Maximum joint strength obtained is 89.71% (as compare to AZ31) which is more than the previously reported joint strengths of dissimilar magnesium alloys. Further, mathematical relations for responses have been developed and utilised for multi-objective optimization using teaching-learning-based optimization algorithm. Eventually, teaching-learning-based optimization algorithm results suggest that the optimum value of surface roughness (3.3925 µm), grain size (12.6869 µm), tensile strength (237.9621 MPa), microhardness (69.3652 Hv) and flexural strength (333.2285 MPa) can be achieved at 921 rpm rotational speed, 30 mm/min welding speed and 15 mm shoulder diameter with overall improvement in multiple responses.

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“…1 A sizable number of studies have been reported highlighting the effect of the rotational and traverse speed on the evolution of FS weld microstructure and its resultant influence on properties. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] During the initial FSW study, Hashimoto et al 19 found that the tool travel and rotational speed affect the quality of joint and formation of the defect. The plunge depth was found to be important in void formation.…”

Section: Introductionmentioning

confidence: 99%

Understanding the effect of tool geometrical aspects on intensity of mixing and void formation in friction stir process

Malik

Kailas

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The reduction of agglomeration of particulate reinforcement in friction stir processing and elimination of joint line remnant in friction stir welding depends on stirring ability of friction stir tool. The magnitude of stirring is indicated by the intensity of material inter-mixing in tool interacted region. Present investigations analyse the influence of geometrical aspects of classical friction stir tool on the intensity of material intermixing, location of high stir regions and void formation during the steady-state phase of friction stir welding. The classical tool was chosen as its geometrical features undergo minimal wear in the harsher processing environment. Investigating material mixing using metallic inserts have their shortcomings due to different flow properties of insert/marker and parent material. Therefore, bi-colour plasticine configuration possessing primary colours was adopted to understand the level of intermixing. The hue attribute of the generated secondary colour was utilised to identify and quantify material mixing. Experimental results revealed that the pin diameter positively influenced mixing and negatively affected void formation. Shoulder size and pin taper angle aided in the closure of void with inferior mixing. The intensity of mixing enhances on selection of tools possessing a shoulder to pin diameter ratio of 2.5. Finite element simulation studies were conducted to understand the reasons for mixing under certain tooling conditions. Since material subjected to stirring is concentrated primarily on a portion of advancing side, components of shear strain, shear strain rates, shear stress and velocity were tracked for this portion of processed material during its interaction with tool pin for one complete rotation.

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Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization

Cited by 14 publications

References 18 publications

Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

Study of optimum welding performance in friction stir welding of dissimilar Mg alloys using integrated RSM-TLBO algorithm

Understanding the effect of tool geometrical aspects on intensity of mixing and void formation in friction stir process

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