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  • 1.
    Afshari, Davood
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    Mechanical Properties of Resistance Spot Welds in Lightweight Applications2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This licentiate thesis is concerned with residual stresses in aluminum alloy 6061-T6 resistance spot welded joint. Several topics related to mechanical strength of welded structures are treated such as; nugget size and microhardness and microstructures of weld zone and their influence on mechanical strength of welded structure, failure load measurement using tensile-shear test, resistance spot welding simulation, residual stress measurement by X-ray diffraction method and analysis effect of welding parameters on the mechanical strength and the residual stresses.

    To investigate the effect of resistance spot weld parameters on mechanical strength of welded structures, various welding parameters e.g. welding current, welding time and electrode force are selected to produce welded joints with different quality. According to the failure mode, the empirical equation was used to prediction of failure load base on nugget size and hardness of failure line. Microstructure study has been carried out to investigate microstructural changes in the welded joints. Microhardness tests are done to find hardness profiles due to microstructural changes and determine the minimum hardness.

    In addition, an electro-thermal-structural coupled finite element model and X-ray diffraction residual stress measurement have been utilized to analyze residual stresses distribution in weld zone. The electrical and thermal contact conductance, as mandatory factors are applied in contact area between electrode-workpiece and workpiece-workpiece to resolve the complexity of the finite element model. The physical and mechanical properties of the material are defined as thermal-dependent in order to improve the accuracy of the model. Furthermore, the electrodes are removed after holding cycle using the birth and death elements method. Moreover, the effect of welding parameters on maximum residual stress is investigated and a regression model is proposed to predict maximum tensile residual stresses in terms of welding parameters.

    The results obtained from the finite element analysis have been used to build up two back-propagation artificial neural network models for the residual stresses and the nugget size prediction. The results revealed that the neural network models created in this study can accurately predict the nugget size and the residual stresses produced in resistance spot weld. Using a combination of these two developed models, the nugget size and the residual stresses can be predicted in terms of spot weld parameters with high speed and accuracy.

  • 2.
    Afshari, Davood
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. Iran University of Science and Technology, Iran.
    Sedighi, M.
    Karimi, M. R.
    Barsoum, Zuheir
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Prediction of residual stresses in resistance spot weld2016In: Aircraft Engineering and Aerospace Technology, ISSN 1748-8842, Vol. 88, no 4, p. 492-497Article in journal (Refereed)
    Abstract [en]

    Purpose - The purpose of this paper is to predict residual stresses in resistance spot weld of 2 mm thick aluminum 6061-T6 sheets. The joint use of finite element analysis and artificial neural networks can eliminate the high costs of residual stresses measuring tests and significantly shorten the time it takes to arrive at a solution. Design/methodology/approach - Finite element method and artificial neural network have been used to predict the residual stresses. Different spot welding parameters such as the welding current, the welding time and the electrode force have been used for the simulation purposes in a thermal-electrical-structural coupled finite element model. To validate the numerical results, a series of experiments have been performed, and residual stresses have been measured. The results obtained from the finite element analysis have been used to build up a back-propagation artificial neural network model for residual stresses prediction. Findings - The results revealed that the neural network model created in this study can accurately predict residual stresses produced in resistance spot weld. Using a combination of these two developed models, the residual stresses can be predicted in terms of spot weld parameters with high speed and accuracy. Practical implications - The paper includes implication for aircraft and automobile industries to predict residual stresses. Residual stresses can lower the strength and fatigue life of the spot-welded joints and determine the performance quality of the structure. Originality/value - This paper presents an approach to reduce the high costs and long times of residual stresses measuring tests.

  • 3.
    Afshari, Davood
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    Sedighi, Mohammd
    Iran Univ Sci & Technol, Tehran, Iran.
    Barsoum, Zuhier
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    Peng, Ru Lin
    Linkoping Tech Univ, Linkoping, Sweden .
    An approach in prediction of failure in resistance spot welded aluminum 6061-T6 under quasi-static tensile test2012In: Proceedings of the Institution of mechanical engineers. Part B, journal of engineering manufacture, ISSN 0954-4054, E-ISSN 2041-2975, Vol. 226, no B6, p. 1026-1032Article in journal (Refereed)
    Abstract [en]

    The aim of this article is to predict the failure load in resistance spot welded aluminum 6061-T6 sheets with 2mm thickness under quasi-static tensile test. Various welding parameters, e. g. welding current, welding time and electrode force are selected to produce welded joints with different quality. The results show that for all the samples in this study only interfacial failure mode was observed in tensile-shear test and no pull-out mode was observed. According to the failure mode, an empirical equation was used for the prediction of failure load based on nugget size and hardness of failure line. Microstructure study has been carried out to investigate microstructural changes in the welded joints. For determination of the minimum hardness, microhardness tests have been carried out to find hardness profiles. The minimum hardness value was observed for a thin layer around the nugget with large and coarse grains. The results show that by using the presented empirical equation, the failure can be predicted with a good agreement only by measuring nugget size.

  • 4.
    Afshari, Davood
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    Sedighi, Mohammd
    Iran Univ Sci & Technol, Tehran, Iran.
    Karimi, M. R.
    Barsoum, Zuhier
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    On Residual Stresses in Resistance Spot-Welded Aluminum Alloy 6061-T6: Experimental and Numerical Analysis2013In: Journal of materials engineering and performance (Print), ISSN 1059-9495, E-ISSN 1544-1024, Vol. 22, no 12, p. 3612-3619Article in journal (Refereed)
    Abstract [en]

    In this study, an electro-thermal-structural-coupled finite element (FE) model and x-ray diffraction residual stress measurements have been utilized to analyze distribution of residual stresses in an aluminum alloy 6061-T6 resistance spot-welded joint with 2-mm-thickness sheet. Increasing the aluminum sheet thickness to more than 1 mm leads to creating difficulty in spot-welding process and increases the complexity of the FE model. The electrical and thermal contact conductances, as mandatory factors are applied in contact areas of electrode-workpiece and workpiece-workpiece to resolve the complexity of the FE model. The physical and mechanical properties of the material are defined as thermal dependent to improve the accuracy of the model. Furthermore, the electrodes are removed after the holding cycle using the birth-and-death elements method. The results have a good agreement with experimental data obtained from x-ray diffraction residual stress measurements. However, the highest internal tensile residual stress occurs in the center of the nugget zone and decreases toward nugget edge; surface residual stress increases toward the edge of the welding zone and afterward, the area decreases slightly.

  • 5.
    Afshari, Davood
    et al.
    School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran .
    Sedighi, Mohammd
    Iran Univ Sci & Technol, Tehran, Iran.
    Karimi, M. R.
    Barsoum, Zuhier
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Lightweight Structures.
    Prediction of the nugget size in resistance spot welding with a combination of a finite-element analysis and an artificial neural network2014In: Materiali in tehnologije, ISSN 1580-2949, E-ISSN 1580-3414, Vol. 48, no 1, p. 33-38Article in journal (Refereed)
    Abstract [en]

    The goal of this investigation is to predict the nugget size for a resistance spot weld of thick aluminum 6061-T6 sheets 2 mm. The quality and strength of spot welds determine the integrity of the structure, which depends thoroughly on the nugget size. In this study, the finite-element method and artificial neural network were used to predict the nugget size. Different spot welding parameters such as the welding current and the welding time were selected to be used for a coupled, thermal-electrical-structural finite-element model. In order to validate the numerical results a series of experiments were carried out and the nugget sizes were measured. The results obtained with the finite-element analysis were used to build up a back-propagation, artificial-neural-network model for the nugget-size prediction. The results revealed that a combination of these two developed models can accurately and rapidly predict the nugget size for a resistance spot weld.

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