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    Effects of asymmetric tooth profile on single-tooth stiffness of polymer gears
    (Walter De Gruyter Gmbh, 2022) Yuce, Celalettin; Dogan, Oguz; Karpat, Fatih
    As a result of polymer materials development and the use of additive manufacturing technologies, gear wheels made of polymer materials are becoming widespread in many areas of the industry. In recent years, determining the dynamic behavior of polymer gears has gained significant importance because it is desired to carry more loads and operate at higher speeds. Since it is one of the most critical factors affecting dynamic behavior, tooth stiffness should also be determined. In this study, the single-tooth stiffness (STS) of polymer gears with symmetrical and asymmetrical profile was measured experimentally with a unique test setup. Force was applied to three different points on the tooth, and the resulting deflection was measured with the help of linear variable differential transformer and a high-speed camera. Using the obtained deflection values, STS of the polymer tooth was calculated depending on the pressure angle. The experimental results are also compared with the finite element model created, and it is found that the results are matched well. As a result of the study, it is determined that the drive-side pressure angle of the polymer gear increased from 20 degrees to 32 degrees, and the tooth stiffness increased by approximately 10.8%.
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    Prognostics and Health Management of Wind Energy Infrastructure Systems
    (Asme, 2022) Yuce, Celalettin; Gecgel, Ozhan; Dogan, Oguz; Dabetwar, Shweta; Yanik, Yasar; Kalay, Onur Can; Ekwaro-Osire, Stephen
    The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy Infrastructure. To address these aspects, four research questions were formulated. What s the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.