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Investigation of joining mechanism of electrical-assist friction stir joining between polyethylene (PE) and 316 stainless steel

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Języki publikacji
EN
Abstrakty
EN
In this study, the Polyethylene (PE) polymer and 316 stainless steel textiles lap welded by the electrical-assist friction stir joining (EA-FSJ) technique. Effects of stirring action of tool and electrical current heat on joining mechanism between PE sheets and 316 stainless steel are investigated. A scanning electron microscope (SEM) was used to investigate of internal material flow. To study the bonding mechanism in joint line, Fourier-transform infrared spectroscopy (FT-IR) and energy-dispersive X-ray spectroscopy (EDS) techniques were employed. Differential scanning calorimetry (DSC) was used to analyze of PE structure after welding. The results show that the joining mechanism in this joint consists of chemical bonding and mechanical locking between 316 stainless steel and PE. The presence of extra heat by electrical current decreased the strength of steel during the welding process and consequently increased chemical bonding and mechanical joining. The final joint flexural and tensile strength were increased by 9 and 10%, respectively, comparing PE raw sheet. The electrical heat increases the PE softening front of the tool and decreases joint thinning after the welding procedure. The mixing between PE and 316 stainless steel fragments increases the stir zone hardness near 84 ShoreD.
Rocznik
Strony
art. no. e199, 2022
Opis fizyczny
Bibliogr. 30 poz., rys., wykr.
Twórcy
  • Department of Mechanics, Design and Industrial Management, University of Deusto, Avda Universidades 24, 48007 Bilbao, Spain
  • Department of Mechanical Engineering, University of Mazandaran, Mazandaran, Iran
  • Department of Mechanical Engineering, University of Mazandaran, Mazandaran, Iran
Bibliografia
  • [1] Derazkola HA, MohammadiAbokheili R, Kordani N, Garcia E, Murillo-Marrodán A. Evaluation of nanocomposite structure printed by solid-state additive manufacturing. CIRP J Manuf Sci Technol. 2022;37:174–84. https://doi.org/10.1016/j.cirpj.2022.01.006.
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  • [5] Lambiase F, Grossi V, Scipioni S, Paoletti A. Prediction of the power supplied in friction-based joining process of metal-polymer hybrids through machine learning. J Manuf Process. 2021;68:750–60. https://doi.org/10.1016/j.jmapro.2021.06.001.
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Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2c38e27e-fad0-4335-8218-f87f7a9c233e
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