Analytical Model for Estimating the Amount of Heat Generated During Friction Stir Welding: Application on Plates Made of Aluminium Alloy 2024 T351

Mijajlović, Miroslav; Milčić, Dragan

doi:10.5772/53563

Cited by 20 publications

(17 citation statements)

References 27 publications

(78 reference statements)

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“…Due to the generated localized heat, the work piece get soften around the probe or pin region which results in the movement of soften or plasticized material from the front part of the probe to the back part of the probe. The welded joint is formed by deforming the material at temperatures below the melting point of parent material [6]. At a very cold welding conditions defects such as void formation and non-bonding can occur while at very hot welding condition issues such as degradation of the strength of the joint can occur as well as there is a formation of collapsible nugget within the stir zone occurs [7].…”

Section: Akshansh Mishra Local Binary Pattern For the Evaluation Of Smentioning

confidence: 99%

Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Mishra

2020

ADCAIJ

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Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.

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Section: Akshansh Mishra Local Binary Pattern For the Evaluation Of Smentioning

confidence: 99%

Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Mishra

2020

ADCAIJ

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“…A Chao model was used in this study. Individual values of energy contributors from equation (1) can be calculated by integrals (2) [28]. It is assumed that all variables, for example, yield strength (σ y ) and thermal conductivity, are independent from temperature and time.…”

Section: Modelmentioning

confidence: 99%

“…This assumption is not in accordance with reality; however, it is sufficient to observe the trends which combine with the temperature flow across the workpiece. The opposite assumption was made by Mijajlovic and Milic [28] who considered the influence of many parameters on generated heat energy, where these parameters are mutually dependent.…”

Section: Modelmentioning

confidence: 99%

Numerical simulation of temperature distribution during the first stage of the friction stir alloying process

2018

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This work demonstrates the numerical modelling of thermal dispersion accompanying the first stage of the friction stir alloying process. It is very important to recognise the temperature field in the modified workpiece in order to identify the zones where the physical material properties are changing. The temperature gradient leads to a drop of yield strength of the material and, as a consequence, the occurrence of the possibility of plastic flow around the tool. an attempt has been made to analyse the axisymmetric thermal problem described by a fourier equation with an internal heat source in which the heat is derived only from work of frictional forces occurring between the workpiece and the tool material. The example under consideration focuses on the production of an al-TiC composite using fsa technology. macrostructure images of the composite and the simulation results confirm the correctness of the applied mathematical model, where the obtained temperature field corresponds with specific fsa zones.

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“…AA2219, AA2519), the major problem is the losses in the participation of the strengthening phase (overaging, dissolution, coarsening) due to heat generated during FSW process [4,7,8]. Although, this phenomenon cannot be avoided for heat is needed for the plasticization of welded material, the proper selection of welding parameters allows to limit the generation of heat, providing only the minimum amount required for the formation of a high-quality joint [9]. In recent years, several investigations have focused on the optimization of welding parameters for AA2219 and AA2519 alloys, including various cooling conditions [7,[10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The influence of welding parameters on macrostructure and mechanical properties of Sc-modified AA2519-T62 FSW joints

et al. 2020

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In this investigation, a 5 mm thick extrusion of AA2519-T62 alloy has been welded using friction stir welding method. The various sets of process parameters have been involved within the range of 400–1200 rpm tool rotation speed and 100–800 mm/min welding speed. Selected joints have been subjected to the macrostructure analysis, microhardness measurements, tensile and low cycle fatigue testing (at ε = 0.3%), and fractography analysis. It has been stated that imperfection-free macrostructure is obtained for welds produced with lowest welding speed: 100 mm/min and tool rotation speed within the range of 400–800 rpm. The highest joint efficiency (85%) has been obtained for the sample characterized by the presence of voids in the upper part of the stir zone. Considering macrostructure analysis and established mechanical properties of the joints, it may be concluded that the best set of welding parameters for AA2519-T62 is within the range of 600–800 rpm tool rotation speed with welding speed of 100 mm/min for used MX Triflute tool.

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Analytical Model for Estimating the Amount of Heat Generated During Friction Stir Welding: Application on Plates Made of Aluminium Alloy 2024 T351

Cited by 20 publications

References 27 publications

Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Numerical simulation of temperature distribution during the first stage of the friction stir alloying process

The influence of welding parameters on macrostructure and mechanical properties of Sc-modified AA2519-T62 FSW joints

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