Influence of the Aluminium Alloy Type on Defects Formation in Friction Stir Lap Welding of Thin Sheets

Costa, Mara; Leitão, C.; Rodrigues, D.M.

doi:10.1590/0104-9224/si2301.04

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…During the μ-FSW process, heat energy is mainly lost to the BP due to the thermal conduction of the BP, which is called heat loss [9]. The Al-Mg-Si series (AA6xxx) and Al-Mg series (AA5xxx) aluminum alloys are used in many fields [10]. The AA6xxx alloys are widely used in various industries due to their excellent workability, corrosion resistance, and low cost.…”

Section: Methodsmentioning

confidence: 99%

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Effect of Backing Plate Materials in Micro-Friction Stir Butt Welding of Dissimilar AA6061-T6 and AA5052-H32 Aluminum Alloys

Park¹,

Joo²,

Kang³

2020

Metals

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Thin sheets of lightweight aluminum alloys, which are increasingly used in automotive, aerospace, and electronics industries to reduce the weight of parts, are difficult to weld. When applying micro-friction stir welding (μ-FSW) to thin plates, the heat input to the base materials is considerably important to counter the heat loss to the jig and/or backing plate. In this study, three different backing-plate materials—cordierite ceramic, titanium alloy, and copper alloy—were used to evaluate the effect of heat loss on weldability in the μ-FSW process. One millimeter thick AA6061-T6 and AA5052-H32 dissimilar aluminum alloy plates were micro-friction stir welded by a butt joint. The tensile test, hardness, and microstructure of the welded joints using a tool rotational speed of 9000 rpm, a welding speed of 300 mm/min, and a tool tilting angle of 0° were evaluated. The heat loss was highly dependent on the thermal conductivity of the backing plate material, resulting in variations in the tensile strength and hardness distribution of the joints prepared using different backing plates. Consequently, the cordierite backing plate exhibited the highest tensile strength of 222.63 MPa and an elongation of 10.37%, corresponding to 86.7% and 58.4%, respectively, of those of the AA5052-H32 base metal.

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Section: Methodsmentioning

confidence: 99%

“…The Al-Mg-Si series (AA6xxx) and Al-Mg series (AA5xxx) aluminum alloys are used in many fields [10]. The AA6xxx alloys are widely used in various industries due to their excellent workability, corrosion resistance, and low cost.…”

Section: Introductionmentioning

confidence: 99%

Effect of Backing Plate Materials in Micro-Friction Stir Butt Welding of Dissimilar AA6061-T6 and AA5052-H32 Aluminum Alloys

Park¹,

Joo²,

Kang³

2020

Metals

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“…In the text, each tool will be identified according to the pin design (CN and CL for the conical and cylindrical pin geometry, respectively) and the pin tip diameter. The welding parameters, which are displayed in Table 2, were defined based on the work conducted by Costa et al [19]. In the next section, the similar welds (S) will be labelled as S6, and the dissimilar welds (D) will be identified according to the base materials positioned in the lap joint.…”

Section: Methodsmentioning

confidence: 99%

Influence of Softening Mechanisms on Base Materials Plastic Behaviour and Defects Formation in Friction Stir Lap Welding

Sabari

Galvão

Leitão

et al. 2020

JMMP

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The AA6082-T6 and AA5754-H22 aluminium alloys were selected as the base materials to fabricate similar and dissimilar friction stir lap welds. Three lap configurations, AA6082/AA5754, AA5754/AA6082 and AA6082/AA6082, were produced using three pin profiles and tested to analyse the role of the plastic behaviours of the base materials on the welding conditions. The macrostructural characterisation was carried out to understand the material flow response and hook defect formation. The mechanical characterisation of the joints was done by microhardness and lap tensile shear testing. The finite element analysis and phase simulation were conducted to predict the phase dissolution temperatures and the softening kinetics. The welding torque and axial forces registered were analysed to quantify differences in the alloy’s flowability during welding. The analysis of the welding machine outputs enabled to conclude that higher axial forces were registered when the AA5754 alloy was placed at the top of the dissimilar lap joint, showing that the non-heat-treatable alloy has lower flowability than the heat-treatable alloy. These results were associated with the flow-softening of the AA6082 alloy in plastic deformation at high temperatures. The coupled experimental and numerical analysis revealed that the plastic behaviour of the base materials strongly influenced the material flow and, in this way, the hook defect formation and the shear tensile properties of the welds.

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“…Any slight variation of the discussed input parameters causes the formation of intermetallic compounds and an external and internal defects like flash formation, groovy edge formation and tunnel defects. Costa et al (2018) compared the weldability of heat treatable AA-6082 T6 aluminium alloy and nonheat treatable AA-5754 H22 aluminium alloy. It is observed that as compare to non-heat treatable aluminium alloy, heat treatable aluminium alloy are more prone to defects formation.…”

Section: Schematic Mechanism Of Materials Flow In Friction Stirmentioning

confidence: 99%

Machine Learning Approach for Defects Identification in Dissimilar Friction Stir Welded Aluminium Alloys AA 7075-AA 1100 Joints

Mishra¹

2020

Journal of Aircraft and Spacecraft Technology

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Machine learning approaches are now applied in various manufacturing industries. Various machine learning algorithms can be implemented for prediction of the particular mechanical properties like Ultimate Tensile Strength (UTS), Elongation percentage and fracture strength of the given mechanical component and also image processing algorithms can be applied for defects detection in the mechanical components. In our recent work, we have used a novel machine learning approach for the detection of the surface defects in dissimilar Friction Stir Welded joints by using Local Binary Pattern (LBP) algorithm. The results obtained are satisfying and it is concluded that the LBP can be implemented in the detection of surface defects.

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Influence of the Aluminium Alloy Type on Defects Formation in Friction Stir Lap Welding of Thin Sheets

Cited by 8 publications

References 21 publications

Effect of Backing Plate Materials in Micro-Friction Stir Butt Welding of Dissimilar AA6061-T6 and AA5052-H32 Aluminum Alloys

Effect of Backing Plate Materials in Micro-Friction Stir Butt Welding of Dissimilar AA6061-T6 and AA5052-H32 Aluminum Alloys

Influence of Softening Mechanisms on Base Materials Plastic Behaviour and Defects Formation in Friction Stir Lap Welding

Machine Learning Approach for Defects Identification in Dissimilar Friction Stir Welded Aluminium Alloys AA 7075-AA 1100 Joints

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