Effect of tool rotational speed on weld quality of friction stir welded AA6061 alloys

Khan, Nasir; Rathee, Sandeep; Srivastava, Manu; Sharma, Chaitanaya

doi:10.1016/j.matpr.2021.07.496

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The weld outcome is influenced by various factors associated with the tool, including its features, material, diameter, and shoulder type. Consequently, the FSW process parameters, such as peak temperature, longitudinal force, torque, and power needs, are set accordingly [2].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

Atmaja,

Safitri,

Rupajati

et al. 2024

MSF

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This study explores the optimization of Micro Friction Stir Spot Welding (mFSSW) by investigating the influence of tool profiles on welding outcomes, using aluminum alloy AA1100 with a 0.42 mm thickness as the specimen material. Monitoring temperature and RPM during welding with thermocouples and tachometers, mechanical properties are assessed through tensile shear tests, microhardness measurements, and macrostructural observations. The findings serve as the basis for developing Neural Network models using Rapidminer software, marking a transformative development that positions Neural Networks as potent tools for optimizing welding processes, potentially leading to achieving optimal weld quality. The investigation also delves into three welding tool configurations – the two-stage pin, one-stage pin, and pinless mFSSW probes – highlighting their distinct impacts on tensile shear test values and overall welding quality. Notably, the two-stage pin configuration emphasizes the significance of larger pin diameters and controlled heat generation for enhanced weld strength, while the one-stage pin configuration underscores the pivotal role of pin diameter and elevated temperatures in improving weld quality. The pinless mFSSW probe configuration, on the other hand, emphasizes the importance of shoulder diameter and temperature control for superior tensile shear test results. Leveraging Neural Network modeling for optimization, this study advances our understanding of parameter interactions and underscores the efficacy of Neural Networks in achieving superior tensile shear test values and welding quality in mFSSW, offering valuable insights for future endeavors in the field..

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Section: Introductionmentioning

confidence: 99%

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

Atmaja,

Safitri,

Rupajati

et al. 2024

MSF

View full text Add to dashboard Cite

show abstract

“…Welding in the modern industrial world is developing rapidly, namely welding in industries engaged in transportation, shipping, railroads, automotive, and construction. All industrial welding engineering applications require quality welds [2,3]. Welding Friction Stir Welding (FSW) is very simple, namely by utilizing the heat energy generated from the friction of the shoulder tool with the material being welded.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Tool Rotation on Structure and Mechanical Properties in Friction Stir Welding Of Aluminum AA5052

Kholis

Purwanto

Annanto

et al. 2022

J. Mod. Mech. Eng. Technol.

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Welding of aluminum alloy materials is often used in the industrial world, including those engaged in shipping, aircraft, and others. The welding method used to bind aluminum materials is Friction Stir Welding (FSW). Friction Stir Welding (FSW) is a simple welding method, namely by utilizing the heat energy generated from the friction of the shoulder tool with the material being welded. This study aimed to determine the effect of tool rotation on the physical and mechanical properties of Friction Stir Welding of Aluminum AA5052. The material used in this study was aluminum with the series AA5052 as the primary material. The friction stir welding process was carried out using CnC milling with parameters of the tool rotation speed of 1000, 1200, and 1500 rpm and a feed rate of 25 mm/minute. Physical analysis was carried out using macro photo observations with a USB Digital Microscope and microstructural observations with optical metallography. Mechanical observations were carried out using tensile testing and hardness testing. The microstructure in the weld nugget area shows good results with fine and dense grains that occur due to the dynamic recrystallization process. The highest tensile strength is 102.49 MPa in the 1000 rpm parameter. This is due to the material mixing process and good heat transfer.

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Analysis of Friction Stir Welding Tool Using Various Threaded Pin Profiles: A Case Study

Abhignya,

Yadav,

Srivastava

et al. 2024

Friction Stir Welding and Processing

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Effect of tool rotational speed on weld quality of friction stir welded AA6061 alloys

Cited by 6 publications

References 32 publications

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

The Effect of Tool Rotation on Structure and Mechanical Properties in Friction Stir Welding Of Aluminum AA5052

Analysis of Friction Stir Welding Tool Using Various Threaded Pin Profiles: A Case Study

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