Friction Stir Welding in the Aerospace Industry

Бойцов, А. Г.; Kuritsyn, D. N.; Siluyanova, M. V.; Kuritsyna, V. V.

doi:10.3103/s1068798x18120043

Cited by 33 publications

(8 citation statements)

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“…The FSW method was used for the production of the wing panels and the fuselage of the Eclipse 500 aircraft, the rear ramp of the Boeing C-17 Globemaster III transport plane, the fuselage panels of the Airbus A380, and the construction of the Boeing 747 Large Cargo Freighter [ 13 ]. The use of the FSW method as a substitute for conventional methods of joining using rivets or bolted connections enables the acceleration of the production process thanks to its automation and the elimination of problems related to making holes for fasteners (notch effect) [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

et al. 2022

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The aim of this research was the selection of friction stir welding (FSW) parameters for joining stiffening elements (Z-stringers) to a thin-walled structure (skin) made of 1 mm-thick EN AW-2024 T3 aluminium alloy sheets. Overlapping sheets were friction stir welded with variable values of welding speed, pin length (plunge depth), and tool rotational speed. The experimental research was carried out based on a three-factor three-level full factorial Design of Experiments plan (DoE). The load capacity of the welded joints was determined in uniaxial tensile/pure shear tests. Based on the results of the load capacity of the joint and the dispersion of this parameter, multi-criteria optimisation was carried out to indicate the appropriate parameters of the linear FSW process. The optimal parameters of the FSW process were determined based on a regression equation assessed by the Fisher–Senecor test. The vast majority of articles reviewed concern the optimisation of welding parameters for only one selected output parameter (most often joint strength). The aim of multi-criteria optimisation was to determine the most favourable combination of parameters in terms of both the smallest dispersion and highest load capacity of the joints. It was found that an increase in welding speed at a given value of pin length caused a decrease in the load capacity of the joint, as well as a significant increase in the dispersion of the results. The use of the parameters obtained as a result of multi-criteria optimisation will allow a minimum load capacity of the joints of 5.38 kN to be obtained with much greater stability of the results.

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Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

et al. 2022

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“…Because the total heat generated during the welding process is below the melting temperature (about 80% of the base material's melting temperature), this welding approach can greatly decrease or eliminate problems that may develop when employing traditional welding techniques, such as severe distortion. FSW is widely used in many industries, mostly in aerospace industry [2], robotics [3], In the shipbuilding and marine industries [4], In the armor industry [5], and the automotive industry [6], and it also permits to weld dissimilar materials combinations [7], [8] The effect of varying friction stir welding parameters such as rotation speed, advance speed and cooling rate on the mechanical properties of aluminum alloy welds was experimentally investigated by several researchers [9] [10].…”

Section: Introductionmentioning

confidence: 99%

A Tree-Driven Ensemble Learning Approach to Predict FS Welded Al-6061-T6 Material Behavior

Dorbane

Harrou

Sun

2022

2022 7th International Conference on Frontiers of Signal Processing (ICFSP)

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This paper proposes a machine learning approach to forecast the mechanical behavior of an aluminum alloy, Al6061-T6, in the case of friction stir welding. Essentially, we investigate the performance of the bagged trees regression (BT) in forecasting the stress-strain curve of an aluminum alloy. This choice's motivation is due to BT's ability to improve the performance of machine learning models by combining multiple learners versus single regressors. Actual data was gathered by performing uniaxial tensile testing on both base material and joined using FSW at a deformation speed of 10 -3 s-1 . Then, the performance of the BT model is compared to that of the Support Vector regression, and it proved to be more accurate.

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“…[ 1–11 ] Friction stir welding (FSW) is a new solid‐phase bonding approach, with advantages such as yielding fewer cracks, lower porosity, and brittleness compared with the traditional melt welding. [ 12–26 ] Benavides et al [ 27 ] studied FSW experiments on the 2024 aluminum alloy; the results showed that welding began at −30 °C but ended at below 140 °C and the weld seam comprised equiaxed grains with an average size of 0.8 μm, in sharp contrast to the 10 μm base‐metal (BM) grains. Kunyu et al [ 28 ] analyzed the comparison of 2A12 aluminum alloy joint by ultrasonic‐assisted FSW (UAFSW) and FSW; the results showed that the grains are much finer in the weld nugget zone (NZ), thermomechanically affected zone (TMAZ), and heat‐affected zone compared with conventional FSW joint.…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Mechanical Properties of Ultrahigh‐Speed Friction‐Stir‐Welded Joints of In Situ Al₃Zr Particle‐Reinforced Aluminum Matrix Composite Sheets

Pinyi

Qiao

et al. 2021

Adv Eng Mater

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Herein, in situ Al3Zr + 6082Al aluminum matrix composite sheets subjected to ultrahigh‐speed friction stir welding (FSW) at welding and stirring‐head rotation speeds of 200 mm min−1 and 11 000, 13 000, or 15 000 rpm, respectively, are focused on. X‐ray diffraction, electron microscopy, energy‐dispersive spectrometry, microhardness testing, and tensile testing are used to investigate the microstructures and mechanical properties of the welds. The weld nugget zone (NZ) of the high‐speed FSW joint of the Al3Zr + 6082Al matrix composite sheet comprises mainly equiaxed recrystallized grains, the thermomechanically affected zone comprises equiaxed recrystallized grains and elongated plastically deformed grains, and the heat‐affected zone comprises recrystallized grains. Results show that the FSW joint formed at 13 000 rpm and 200 mm min−1 is extremely well formed and has a compact and uniform structure. The microhardness of the 13 000 rpm FSW joint is highest at an NZ hardness and tensile strength of 128.08 HV and 153.7 MPa (87% of that of the base metal), respectively.

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Friction Stir Welding in the Aerospace Industry

Cited by 33 publications

References 1 publication

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

A Tree-Driven Ensemble Learning Approach to Predict FS Welded Al-6061-T6 Material Behavior

Microstructure and Mechanical Properties of Ultrahigh‐Speed Friction‐Stir‐Welded Joints of In Situ Al₃Zr Particle‐Reinforced Aluminum Matrix Composite Sheets

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Friction Stir Welding in the Aerospace Industry

Cited by 33 publications

References 1 publication

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints

A Tree-Driven Ensemble Learning Approach to Predict FS Welded Al-6061-T6 Material Behavior

Microstructure and Mechanical Properties of Ultrahigh‐Speed Friction‐Stir‐Welded Joints of In Situ Al3Zr Particle‐Reinforced Aluminum Matrix Composite Sheets

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Microstructure and Mechanical Properties of Ultrahigh‐Speed Friction‐Stir‐Welded Joints of In Situ Al₃Zr Particle‐Reinforced Aluminum Matrix Composite Sheets