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    NEURAL PREDICTOR DESIGN FOR COVID-19 CASES IN DIFFERENT REGIONS
    (Cefin Publishing House, 2023) Yildirim, Sahin; Durmusoglu, Aslı; Sevim, Caglar; Bingol, Mehmet Safa; Kalkat, Menderes
    COVID-19, which emerged in the past years, has affected human life in many different ways. The COVID-19 virus has spread very quickly around the world and has become a pandemic. In many applications, artificial neural networks are used to estimate system parameters in real-time or simulation-based methods. In this study, the daily and total number of cases in Turkey, Italy and India are predicted. Three alternative areas, with or without following rules, are chosen for the COVID-19 cases. For this prediction process, 3 different neural network methods are used: Nonlinear autoregressive neural network (NAR-NN), Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Autoregressive integrated moving average (ARIMA). The results obtained for 3 different neural networks are given with graphs and tables. The conclusion of this study may be used to improve the precaution for the pandemic. © 2023, Cefin Publishing House. All rights reserved.
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    Numerical Modeling of Mechanical Behavior of Functionally Graded Polylactic Acid-Acrylonitrile Benzidine Styrene Produced via Fused Deposition Modeling: Experimental Observations
    (Mdpi, 2023) Sevim, Caglar; Caliskan, Umut; Demirbas, Munise Didem; Ekrikaya, Safa; Apalak, Mustafa Kemal
    Functionally graded materials (FGM) have attracted considerable attention in the field of composite materials and rekindled interest in research on composite materials due to their unique mechanical response achieved through material design and optimization. Compared to conventional composites, FGMs offer several advantages and exceptional properties, including improved deformation resistance, improved toughness, lightness properties, and excellent recoverability. This study focused on the production of functionally graded (FG) polymer materials by the additive manufacturing (AM) method. FG structures were produced by the fused deposition modeling (FDM) method using acrylonitrile benzidine styrene (ABS) and polylactic acid (PLA) materials, and tensile tests were performed according to ASTM D638. The effects of different layer thicknesses, volume ratios, and total thicknesses on mechanical behavior were investigated. The tensile standard of materials produced by additive manufacturing introduces geometric differences. Another motivation in this study is to reveal the differences between the results according to the ASTM standard. In addition, tensile tests were carried out by producing single-layer samples at certain volume ratios to create a numerical model with the finite element method to verify the experimental data. As a result of this study, it is presented that the FG structure produced with FDM improves mechanical behavior.
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    Tensile behavior of functionally graded sandwich PLA-ABS produced via fused filament fabrication process
    (Taylor & Francis Inc, 2024) Caliskan, Umut; Sevim, Caglar; Demirbas, Munise Didem
    The study investigated the tensile behavior of Sandwich Functionally Graded Material (SFGM) fabricated using Additive Manufacturing (AM) technology experimentally and numerically. SFGMs are characterized by a gradual variation in composition and structure with respect to the forming volume from the lower and upper surfaces of the structure toward the center, resulting in a corresponding change in material properties. Fused Filament Fabrication (FFF), a widely used AM process, was used in the present work to fabricate the thermoplastic polymer-based SFGM specimens. SFGM were produced by the FFF method using ABS and PLA materials and subjected to tensile tests according to ASTMD638.