Study on mechanical properties of 2219 aluminum alloy bobbin tool friction stir welding

Li, Xitong; An, Tiancheng; Lan, Jinlong; Hu, Xia; Hu, Jiaqi

doi:10.1088/2053-1591/acf09f

Cited by 3 publications

(2 citation statements)

References 47 publications

(47 reference statements)

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“…Elemental analysis of the precipitate phase using EDS revealed that aluminum (Al) accounts for 59.22%, while copper (Cu) accounts for 40.78%. It is speculated that this is Al2Cu, which aligns with the results obtained by other scholars [19][20][21][22][23]. The TMAZ, as shown in Figure 4b,e at different magnifications, undergoes stirring and welding thermal cycles.…”

Section: Analysis Of Microscopic Properties and Partition Of 2219 Alu...supporting

confidence: 90%

Study on the Properties of a 2219 Aluminum Alloy Friction Stir-Welded Joint under the Biaxial Stress State

Shao,

Wang,

Huang

et al. 2023

Metals

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Friction stir welding (FSW) has been considered ideal for aluminum alloy structures. The performance of FSW joints under unidirectional stress state has been widely tested, but there is still a lack of experimental data under a biaxial stress state. For accurate characterization of the mechanical properties of 2219 aluminum alloy FSW joints under a biaxial stress state, this article conducted biaxial tests. The FSW joint was tested using scanning electron microscopy (SEM) to obtain its microscopic properties and obtain the partition results of the welded joint. The stress–strain relationship and yield characteristics of welded joints under different loading ratios were studied using biaxial tensile tests of cruciform specimens combined with digital image correlation technology. By comparing it with the existing yield criteria, the yield criterion suitable for FSW joints of 2219 aluminum alloy under a biaxial tensile load was determined.

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Section: Analysis Of Microscopic Properties and Partition Of 2219 Alu...supporting

confidence: 90%

Study on the Properties of a 2219 Aluminum Alloy Friction Stir-Welded Joint under the Biaxial Stress State

Shao,

Wang,

Huang

et al. 2023

Metals

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“…This is caused by the occurrence of intermetallic Al 3 Sc in the WN as well as inexplicable Sc elements in the matrix of aluminium. Due to differences in microstructure, the TMAZ and HAZ displayed lesser hardness [39][40][41].…”

Section: Microhardnessmentioning

confidence: 99%

Influence of scandium interlayer on the mechanical and metallurgical characteristics of friction stir welded AA1200-H14/ Sc/AA6061-T6

Arunkumar,

Vijay Sekar

2024

Mater. Res. Express

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The present study examined the friction stir welding (FSW) of joints made from the interfacially layered dissimilar AA1200-H14 and AA6061-T6 using scandium about the UTS, frictional heat, and metallurgical properties. The experimental examination is carried out utilising a three-level, three factors, Box-Behnken Design matrix (17 tests) in response surface approach, with the welding speed (WS), axial force (AF), and tool rotation speed (TRS) as the stated input parameters. Analysis of variance (ANOVA) was used to evaluate the model's dependability. Macroscopic and microscopic joint defects, as well as the alloy intermixture, have been found using optical microscopy and SEM. The optimal WS (50 mm/min), AF (10 kN) and TRS (1750 rpm) settings produced the maximum terminal tensile strength (200.182 MPa) and perfect frictional heat observations. The inclusion of scandium interlayer as well as optimised parameters improved the joint's mechanical characteristics and produced a fine-grained microstructure.

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Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

Yang,

2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.

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Study on mechanical properties of 2219 aluminum alloy bobbin tool friction stir welding

Cited by 3 publications

References 47 publications

Study on the Properties of a 2219 Aluminum Alloy Friction Stir-Welded Joint under the Biaxial Stress State

Study on the Properties of a 2219 Aluminum Alloy Friction Stir-Welded Joint under the Biaxial Stress State

Influence of scandium interlayer on the mechanical and metallurgical characteristics of friction stir welded AA1200-H14/ Sc/AA6061-T6

Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

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