Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
2 results
Search Results
Conference Object Citation - WoS: 1Citation - Scopus: 1Modeling a Magneto-Rheological Fluid-Based Brake Via a Neural Network Method(Springer international Publishing Ag, 2022) Kucukoglu, Sefa Furkan; Dede, Mehmet Ismet Can; Ceccarelli, MarcoIdentifying the model of a magneto-rheological (MR) fluid-based brake is extremely important for designing and controlling a haptic device with hybrid actuation. Therefore, in this study, an Elman Recurrent Neural Network (ERNN) is designed to understand and model a characterization of an MR fluid-based rotational brake. Three important factors that affect the MR brake's performance are chosen as inputs: current, speed, and the first derivative of the input current. The proposed network is trained, and the performance of the network is tested with three different experimental scenarios. Then, the effect of these inputs on the system is investigated. According to the results, it can be said that the designed ERNN is a good candidate for modelling an MR brake.Conference Object Citation - WoS: 2Citation - Scopus: 2Function Generation Synthesis of Planar Slider-Crank Linkages for Given 3 Positions and a Dead-Center Position(Springer Verlag, 2020) Kiper, Gokhan; Gorgulu, Ibrahimcan; Kucukoglu, Sefa FurkanFunction generation for finitely many positions and dead-center design problems are generally separately handled in the literature. This paper presents a mixed formulation for planar slider-crank linkages where three precision points and a folded or extended dead-center position are to be satisfied. The formulation results in an 8th degree univariate. Examples show that generally there are four real solutions, only two of which result in distinct solutions.