Analysis of process parameters effects on underwater friction stir welding of aluminum alloy 6082-T6

Wahid, Mohd Atif; Khan, Zahid A.; Siddiquee, Arshad Noor; Shandley, Rohit; Sharma, Nidhi

doi:10.1177/0954405418789982

Cited by 46 publications

(27 citation statements)

References 27 publications

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“…1–3 For these reasons, industrial and academic groups have produced modern polymeric structures with FSW process. 4–6 In this process, frictional heat is generated by tool shoulder and pin causing material change into plastic phase. Rotational and traverse direction of the tool causes the plasticized material extrude from the leading edge (LE) into stir zone (SZ) and form joint area.…”

Section: Friction Stir Welding Tool Plunge Depth Tool Tilt Angle Mmentioning

confidence: 99%

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Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Eyvazian

Derazkola

Elyasi

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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The effects of tilt angle (TTA), plunge depth (TPD) and offset (TO) of tool in friction stir welding of poly(methyl methacrylate) T-joint were investigated. To understand better the effects of process parameter, thermomechanical simulation of joint was assessed. The results seem to show that at higher TPD and TTA, frictional heat increases. Woven tissue structure joint line forms after friction stir welding of poly(methyl methacrylate) sheets. The distance of woven layers was affected by TPD and TTA, while TO do not significantly affect heat generation of joint. The best material flow and adequate heat are generated at 0 mm TA, 2° TTA and 0.2 mm TPD, respectively. The highest flexural and tensile strength of friction stir welded joint were approximately 93% and 90% of as-received poly(methyl methacrylate), respectively. Crack forking was detected on the fractured surface of flexural samples and crack path was detected in the vicinity of shrinkage holes at fracture surface of tensile samples. These holes and degradation of poly(methyl methacrylate) during friction stir welding process decrease strength and hardness of the joint.

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Section: Friction Stir Welding Tool Plunge Depth Tool Tilt Angle Mmentioning

confidence: 99%

“…During FSW, frictional heat changes the hardness of base materials. [35][36][37] The hardness changes can be completely different between metallic materials and polymeric materials. For this reason, differential scanning calorimetry (DSC) analysis was carried out on PMMA sample to find root of hardness changes in SZ.…”

Section: Hardnessmentioning

confidence: 99%

Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Eyvazian

Derazkola

Elyasi

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Friction stir (spot) welding is a novel solid-state joining process (pioneered by The Welding Institute in the 1990s) that has become a suitable substitute for conventional fusion welding processes by eliminating improper solidification and intrinsic liquation cracking, weld discontinuities (micro-and macro-pores, and inclusions), and distortion due to high heat input [1][2][3][4]. Friction stir (spot) welding has found a broad application in offshore construction (orbital welding of pipelines), automotive, high-speed train manufacturing, shipbuilding, and aerospace industries [5,6].…”

Section: Introductionmentioning

confidence: 99%

Cyclic lateral behavior of friction stir spot welds of AA2219 aluminum alloy: impact of inherent flow defects

Ojo¹,

Taban²,

Kaluç³

et al. 2021

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Structural damage and fatigue life of engineering materials are controlled by inherent defects and the nature, level, and location of applied external loads. This paper studies the effect of cyclic lateral loads on the pinless and conical-pin assisted friction stir spot welds of AA2219 alloy. The results reveal the presence of upward-flow induced hook defect and outstretchingflow induced microscopic toe-notch in the conical pin and pinless welds, respectively. Extruded flash root contributed to the fracture pattern of the conical pin welds while it did not influence the fracture of the pinless weld. Grain refinement, microhardness, and microscopic toe-notch affect the fracture mode of pinless weld whereas complex fracture morphology ensues in conical pin weld. A decrease in the cyclic lateral stress level improves the lateral fatigue lives of both weld categories. Lateral fatigue improvement of welds requires the minimization of inherent nugget defects and extruded flash.

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“…Friction stir welding (FSW) is an efficient method to join similar and dissimilar materials as melting and recasting of the material are not involved in this process. 3–5 Various heat-treatable aluminum alloys (HTAAs), whose joining is relatively challenging using FW, can also be effectively welded with FSW. 3 This technique announced in the 1990s is mainly based on frictional heating along with extreme plastic deformation of the base material (BM) for the joining of different pieces together.…”

Section: Introductionmentioning

confidence: 99%

A simulation-based study on the effect of underwater friction stir welding process parameters using different evolutionary optimization algorithms

Wahid

Masood

Khan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper investigates the effect of underwater friction stir welding process parameters on the mechanical properties of the aluminum alloy 6082-T6 joint and further simulates this process using various evolutionary optimization algorithms. Three independent underwater friction stir welding process parameters, i.e. shoulder diameter (in mm) at two levels, rotational speed (in r/min) at three levels, and traverse speed (in mm/min) also at three levels, were varied according to the Taguchi’s L18 standard orthogonal array. The effect of variations in these parameters, on the ultimate tensile strength (in MPa), percentage elongation (in %), and impact strength (in J) of the welded joint was experimentally measured and recorded. In order to simulate this underwater friction stir welding process, three evolutionary optimization algorithms, i.e. particle swarm optimization, firefly optimization, and non-dominated sorting established on the genetic algorithm (NSGA-II), were employed. In these simulations, an artificial neural network with two layers, resembling a non-linear function, was employed as the cost function to predict the values of the response variables, i.e. ultimate tensile strength, elongation, and impact strength, which were experimentally measured earlier. In these simulations, several experiments were conducted using different randomly selected data set and subsequently, the accuracy of each individual simulation was compared. Results revealed that the firefly optimization-based simulation performed the best with least mean squared error while predicting the response variable values, as compared to the particle swarm optimization and the NSGA-II. The minimum value of the mean squared error for the firefly optimization-based simulation was observed to be as low as 0.009%, 0.004%, and 0.017% for ultimate tensile strength, elongation and impact strength, respectively. Furthermore, it was also observed that the computational time for the firefly optimization-based simulation was significantly lower than that of both particle swarm optimization and NSGA-II-based simulations.

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Analysis of process parameters effects on underwater friction stir welding of aluminum alloy 6082-T6

Cited by 46 publications

References 27 publications

Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Cyclic lateral behavior of friction stir spot welds of AA2219 aluminum alloy: impact of inherent flow defects

A simulation-based study on the effect of underwater friction stir welding process parameters using different evolutionary optimization algorithms

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