Mechanical properties of AA6061T6 and AA6351T6 plates joined by friction stir welding

Prasad, Lalta; Kumar, Ajay; Jaiswal, Ragesh; Kumar, Vinod; Yadav, Anshul

doi:10.1002/mawe.202100108

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…11 The concepts of frictional heating and plastic deformation are combined in FSW, a cutting-edge solid-state joining method in which a thermomechanical way is used to plasticize materials. [12][13][14][15][16] The stirring results in mixing, and later a joint is developed at a temperature below the melting point of the base materials. 17 Thus, FSW is a better joining process for decreasing distortions and residual stresses that affect traditional welding processes.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network

Adarsh

Natarajan

2022

Journal of Thermoplastic Composite Materials

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Lightweight hybrid structures are developing these days due to increased demand for fuel economy and lower emissions in the automotive and aerospace industries. This study aims to analyse and optimise the influence of friction stir welding (FSW) process parameters on the tensile shear strength of the aluminium-polyamide hybrid joint. The study on the influence of each parameter on the joint strength helps define the bonding mechanism while joining aluminium-polymer hybrid structures. Optical microscopy and scanning electron microscopy (SEM) were used for microstructural examination. A SEM image of the weld’s cross-sectional area shows micro and macro mechanical interlocks with a small interfacial gap which indicates better joint strength. An elemental area mapping investigation of the weld zone reveals fine polymer and aluminium mixing along the interaction region. In addition, FSW parameters have been optimized to maximize the tensile shear strength of aluminium-polyamide hybrid joints. A mathematical model for tensile shear strength in terms of FSW parameters is developed using response surface methodology (RSM). A predictive model was developed using an Artificial Neural Network (ANN) to validate RSM predicted results. The analysis of variance (ANOVA) shows that the actual and predicted values have a satisfactory correlation. ANN methods are better than regression models in predicting tensile shear strength within input welding parameter ranges. The process variables were optimised using the desirability function analysis. The maximum joint tensile shear strength of about 19.74 MPa and attained at optimal FSW parameters, i.e. rotational tool speed of 1421 r/min, welding speed of 27 mm/min, and tool tilt angle of 1°. The regression coefficient for the ANN model was 0.988 for the test data set, indicating that the developed model is appropriate for predicting tensile shear strength.

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Section: Introductionmentioning

confidence: 99%

Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network

Adarsh

Natarajan

2022

Journal of Thermoplastic Composite Materials

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Corrosion, Fatigue, and Wear Performance of Friction Stir Welded Aluminum Metal Matrix Composites: A Review

Senthilraj

Rajamurugan

2023

Trans Indian Inst Met

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Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction

Devanathan,

Elil Raja,

Sonar

et al. 2024

Advances in Materials Science and Engineering

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The friction stir welding (FSW) method was used to weld B4C reinforced AA 5083 metal matrix composites in this study. By coating titanium nitride (TiN), aluminium chromium nitride (AlCrN), and diamond-like carbon (DLC) to a thickness of 4 microns, three FSW tools with square pin profiles were developed and the friction coefficients of 0.69, 0.32, and 0.2 were maintained. At three levels, the process factors such as tool rotating speed, transverse feed, and axial force were examined. For each tool, 15 samples were made using the central composite design. The influence of the friction coefficient on ultimate tensile strength, microstructural features, and tool condition was studied, and the flower pollination algorithm (FPA) technique was used to find the best process parameters for obtaining maximum ultimate tensile strength of FSW joints. The improved tensile strength of FSW joints was verified using a validation test. The coating has a considerable influence on the ultimate tensile strength, microstructure, and tool condition, according to the results of the tool’s friction coefficient. The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective.

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Mechanical properties of AA6061T6 and AA6351T6 plates joined by friction stir welding

Cited by 3 publications

References 38 publications

Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network

Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network

Corrosion, Fatigue, and Wear Performance of Friction Stir Welded Aluminum Metal Matrix Composites: A Review

Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction

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