Browsing by Author "Ilman M.M."

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    Covid-19 optimization-based vibration controller for a flexible manipulator
    (IGI Global, 2022) Ilman M.M.; Arikuşu Y.S.
    Flexible manipulator real-time vibration control methods are effective, but finding the right control gain is difficult. The reason for this is that traditional approaches are not permitted by up-to-date and novel architectures. Current population-based meta-heuristic optimization approaches, on the other hand, can provide solutions for such challenges, as they are inspired by many natural phenomena. Therefore, in the study, the Coronavirus herd immunity optimization (CHIO) method, inspired by the herd immunity mechanism, which is a COVID-19 control method, was used for the optimization of a flexible manipulator control gains. Gray-wolf-optimizer (GWO), another up-to-date population-based algorithm, and traditional particle swarm optimizer (PSO) were used to compare the success of the method. The findings reveal that when it comes to optimizing the vibration controller gains of flexible manipulators, CHIO can outperform its contemporary and traditional competitors. © 2023 by IGI Global. All rights reserved.
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    Design optimization application for a flexible robot manipulator
    (IGI Global, 2022) Ilman M.M.; Yavuz S.; Akgöl D.
    In this chapter, design optimization is performed for a single-link flexible manipulator driven by a standard trapezoidal velocity input. The mass of the original manipulator is reduced by a quarter by design optimization without changing the payload. The finite element model, which is one of the non-rigid models, and topology optimization were utilized for the application. Dynamic stress and natural frequencies of the system are utilized as optimization objectives. The results were compared with the original manipulator in terms of both safety factor and vibration modes. In addition, the system parameters were compared with the tapered beam, and beam with reduced width, which was prepared to have the same mass as the optimized designs. As a result, although the mass of the original manipulator was reduced by one-fourth with the proposed design, it was observed that the dynamic stresses decreased. The study is expected to have significant implications in terms of improving the benefits of flexible robots and providing a contribution to the attenuation of vibrations and stresses. © 2023 by IGI Global. All rights reserved.
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    A soft robotic gripper material study: Effects of CNT mixing methodologies
    (IGI Global, 2022) Ilman M.M.; Taş H.
    Soft robots have gained superiority in outdoor applications compared to traditional robots today. This advantage is clearly due to bio-inspiration and evolving material technology. The objective of this research is to use nanotechnology to improve material qualities. For this, silicone named DragonSkin 20 (DS20), which can be employed in soft robot applications, was selected as the matrix material, while functionalized multi-walled-carbon-nanotube (MWCNT) was utilized as an additive. One of the parameters that determine the mechanical properties is the change of curing behavior. The choice of mixing technique, on the other hand, is very crucial since it affects the curing behavior. For this reason, the effects of not only the additive but also the various mixing techniques on the material behavior and curing time were reported as a result of the experiments. The results showed that the mixing methodologies plays an important role on the mechanical properties and curing time of neat and MWCNT reinforced silicone. © 2023, IGI Global.
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    Machine learning and optimization applications for soft robotics
    (IGI Global, 2022) Ilman M.M.; Taser P.Y.
    Due to their adaptability, flexibility, and deformability, soft robots have been widely studied in many areas. On the other hand, soft robots have some challenges in modeling, design, and control when compared to rigid robots, since the inherent features of soft materials may create complicated behaviors owing to non-linearity and hysteresis. To address these constraints, recent research has utilized different machine learning algorithms and meta-heuristic optimization techniques. First and foremost, the study looked at current breakthroughs and applications in the field of soft robots. Studies in the field are grouped under main headings such as modelling, design, and control. Fundamental issues and developed solutions were analyzed in this manner. Machine learning and meta-heuristic optimization-oriented methods created for various applications are highlighted in particular. At the same time, it is emphasized how the problems in each of the modeling, design, and control areas impact each other. © 2023, IGI Global.