Large Deflection Analysis of Functionally Graded Reinforced Sandwich Beams With Auxetic Core Using Physics-Informed Neural Network

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2025

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Taylor & Francis inc

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Automotive Engineering
(2009)
Having started education in 2009, the Atılım university Department of Automotive Engineering offers an academic environment at international standards, with its education in English, a contemporary curriculum and ever-better and ever-developing laboratory opportunities. In addition to undergraduate degree education, the graduate program of multi-disciplinary mechanical engineering offers the opportunity for graduate and doctorate degree education automotive engineering. The Atılım University Automotive Engineering has been selected to be the best in Turkey in 2020 in the field of automotive engineering with studies in energy efficiency, motor performance, active/ passive automotive security and vehicle dynamics conducted in the already-existing laboratories of its own. Our graduates are employed at large-scale companies that operate in Turkey, such as Isuzu, Ford Otosan, Hattat, Honda, Hyundai, Karsan, Man, Mercedes-Benz, Otokar, Renault, Temsa, Tofaş, Toyota, Türk Traktör, Volkswagen (to start operation in 2020). In addition, our graduates have been hired at institutions such as Tübitak, Tai, Aselsan, FNSS, Ministry of National Defence, Tcdd etc. or at supplier industries in Turkey. Due to the recent evolution undergone by the automotive industry with the development of electric, hybrid and autonomous vehicle technologies, automotive engineering has gained popularity, and is becoming ever more exhilarating. In addition to combustion engine technologies, our students also gain expertise in these fields. The “Formula Student Car” contest organized since 2011 by the Society of Automotive Engineers (SAE) where our Department ranked third globally in 2016 is one of the top projects conducted by our department where we value hands-on training. Our curriculum, updated in 2020, focuses on computer calculation and simulation courses, as well as laboratory practice, catered to modern automotive technologies.

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This paper aims to investigate the large deflection behavior of a sandwich beam reinforced with functionally graded (FG) graphene platelets (GPL) together with an auxetic core, rested on a nonlinear elastic foundation. The nonlinear governing equations of the problem are derived using Hamilton's principle based on the Euler-Bernoulli beam theory for large deflections. Five different distributions are considered to describe the dispersion of GPL in the top and bottom faces of the sandwich beam. The Physics-Informed Neural Network (PINN) method is employed to model the nonlinear deflection of the beam under various boundary conditions. This study highlights the effectiveness of PINN in handling the complexities of nonlinear structural analyses. The findings underscore the impact of the core auxeticity, GPL amount and distribution, and elastic foundation coefficient on the nonlinear deflection of the sandwich beam under different loading scenarios. For instance, using Type I configuration can reduce the deflection of the beam by nearly half compared to using Type IV. Furthermore, a nonlinear foundation with a unit coefficient results in a 48% reduction in deflection compared to the scenario without an elastic foundation.

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Nonlinear Bending, Auxetic Composites, Physics-Informed Neural Network, Sandwich Beam

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