Experimental study on the role of tool geometry in determining the strength &amp; soundness of wrought Az80a Mg alloy joints during FSW process

Thangaiah, I.S. Stephan; Sevvel, P.; Satheesh, C.; Mahadevan, S.

doi:10.5937/fmet1804612t

Cited by 12 publications

(4 citation statements)

References 53 publications

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“…All friction stir welded joints exhibited a basin‐shaped stir zone, Figure 5. The tool pin profile determines the stir zone shape, controlling the material flow in the pin‐affected area [31, 39]. The stir zone exhibited a similar geometry at the FSW‐1, FSW‐4 and FSW‐7 welded joints.…”

Section: Resultsmentioning

confidence: 99%

Effects of parameters on friction stir welding process of AA 7075 aluminum alloy: mechanical and microstructural assessments

Ozan

Şahin

Kasman

2022

Materialwissenschaft Werkst

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The effect of tool rotation speed and welding speed on the microstructural and mechanical properties of AA 7075-T651 welded plates produced using friction stir welding is the research subject of this study. A basin-shaped stir zone characterized all friction stir welded joints' microstructures. The fracture location following the tensile test was found to be outside the stir zone for each welded joint. All welded joints were subjected to a 90°root bending test. The tests were terminated before each welded joint could reach the 90°bend angle in the stir zone. The stir zone of each welded joint was characterized by the fine and equiaxed grains with the effect of dynamic recrystallization. In addition, intermetallic particles were also detected to occur in the weld joint. Among the nine welded joints, the welded joint with the highest ultimate tensile strength of 382 MPa was produced with a tool rotation speed of 400 min À 1 and a welding speed of 80 mm min À 1 . The highest welding efficiency of 71 % was obtained with the welding process parameters of 400 min À 1 tool rotational speed and 80 mm min À 1 welding speed. Increasing tool rotation speed caused the grain size to be coarser.

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Section: Resultsmentioning

confidence: 99%

Effects of parameters on friction stir welding process of AA 7075 aluminum alloy: mechanical and microstructural assessments

Ozan

Şahin

Kasman

2022

Materialwissenschaft Werkst

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“…Alloys of Copper have the immense capability for usage in a variety of industrial sectors including marine systems, construction, transportation, for encapsulating material wastages especially nuclear wastes etc and this usage is possible mainly because of their 2 superior thermal and electrical conductivities, a unique combination of ductility and strength, exemplary resistance to corrosion etc [1][2][3][4][5] Even though there exists an everlasting demand for the alloys of copper (Cu) in several industrial sectors, the usage of these alloys are restricted due to the difficulty in welding them [6][7][8][9]. Fusion-based joining techniques are not suitable for copper, as the existence of pernicious and eruptive constituents such as zinc (present in the alloys of Cu) and other oxides (adherent in nature) deteriorate the quality of weld [10][11][12][13]. In addition to this, the peculiar characteristic features of alloys of Cu namely, larger level of thermal diffusivity, elevated oxidation rate, etc also hinder the successful welding of alloys of Cu by fusion-based joining techniques [14,15].…”

Section: Introductionmentioning

confidence: 99%

Impact of speed of traverse during joining of CDA101 plates by FSW process

Kumar¹,

Sevvel²

2022

Scientia Iranica

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An experimental investigation was carried out to comprehend the impact of speed of traverse of tool on tensile strength and micro-structural peculiarities of attained joints during friction stir welding of Cu alloy namely CDA 101 flat plates, with other parameters namely spinning speed of tool (1100 rpm) and downward force (6kN) being constant. A tool with cylindrical tapered pin geometry was made to traverse at distinctive speeds from 20 mm/min to 45 mm/min. It was observed that, the CDA 101 joints fabricated at 20 mm/min was found to be entirely free flaws, with joints fabricated at other speeds of tool traverse possessing several weld flaws. Grains in the center of the zone of stir of the joints obtained at 20 mm/min was uniformly distributed and homogeneous, due to the experience of the exemplary volume of frictional heat and sufficient amount of stirring force. Highest tensile strength of 200.65 MPa (nearly 85.38% of base metal)was exhibited by joint attained at 20 mm/min.

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“…Building a mathematical model according to the actual problem, solving the mathematical model and then solving the actual problem according to the results are widely used in various fields [7][8][9]. An artificial neural network is also a kind of mathematical modeling, which makes predictions after building models.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN

Liu

2022

Coatings

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In order to solve the problem of accurate and efficient detection of welding defects in the process of batch welding of metal parts, an improved Probabilistic Neural Network (PNN) algorithm was proposed to build an automatic identification model of welding defects. Combined with the characteristics of the PNN model, the structure and algorithm flow of the FAST-PNN algorithm model are proposed. Extraction of welding defect image texture features of metal welded parts by a Gray Level Co-occurrence Matrix (GLCM) screens out the characteristic indicators that can effectively characterize welding defects. Weld defect texture features are used as input to build a defect classification model with FAST-PNN, for accurate and efficient classification of welding defects. The results show that the improved FAST-PNN model can effectively identify the types of welding defects such as burn-through, pores and cracks, etc. The classification recognition accuracy and recognition efficiency have been significantly improved. The proposed defect welding identification method can accurately and effectively identify the damage types of welding defects based on a small number of defect sample images. Welding defects can be quickly identified and classified by simply collecting weld images, which helps to solve the problem of intelligent, high-precision, fast real-time online detection of welding defects in modern metal structures; it provides corresponding evidence for formulating response strategies, with a certain theoretical basis and numerical reference.

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Experimental study on the role of tool geometry in determining the strength & soundness of wrought Az80a Mg alloy joints during FSW process

Cited by 12 publications

References 53 publications

Effects of parameters on friction stir welding process of AA 7075 aluminum alloy: mechanical and microstructural assessments

Effects of parameters on friction stir welding process of AA 7075 aluminum alloy: mechanical and microstructural assessments

Impact of speed of traverse during joining of CDA101 plates by FSW process

Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN

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