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Design of a Robot Assisted Minimally Invasive Surgical System for Pituitary Tumor Surgery Based on Safety Features
(Izmir Institute of Technology, 2020) Maaroof, Omar Waleed Najm; Dede, Mehmet İsmet Can
The study is on the designing a robot assisted endonasal endoscopic surgical system; NeuRoboScope, the pituitary tumor resection surgery system. This system comprises a passive and an active arm. The passive arm positions the active arm in the surgery zone while the active arm assists the surgeon by positioning the endoscope during the surgery. The focus of this thesis is the mechanical and control safety features that can be implemented in the system. The safety enhancement methods of robot assisted minimally invasive surgery systems are investigated. Among the seventeen robot assisted endoscope holders, sixteen of them have been implemented in pituitary tumor and sinus surgeries. Safety is the main criterion that advances the progress of these systems and places them in operation rooms. Accordingly, two optimization procedures have been applied during the design of the NeuRoboScope system that have a direct effect on the suggested safety features. A novel optimization technique is proposed by employing a redundancy resolution method. The most suitable fixing point of the passive arm and its first link length is optimized to achieve the maximum manipulability with restrictions imposed by a modified condition number index and impedance of the passive arm. The active arm's partial gravity compensation is studied. Three spiral springs are used as counter-springs as the most compact and lightweight partial gravity compensation method. Particle swarm optimization method is employed for the optimization of the design parameters: spiral spring stiffnesses and preload angles. Consequently, at least 66% of actuator loads are compensated.
Aerodynamic Optimization of a Transonic Aero-Engine Fan Module
(Izmir Institute of Technology, 2016) Kor, Orçun; Özkol, Ünver
Aerodynamic design of an aero-engine fan blade is a multi-step process with multi-variables. The general purpose in aerodynamic design is to obtain proper blade angles and flowpath geometry providing the necessary pressure ratio with maximum efficiency, while respecting the structural and aerodynamic constraints. The throughflow design in aerodynamic design procedure is a key step where one can obtain a basic aero-design which generally fixes 80% to 90% of the final fan geometry, by adjusting parameters like blade exit angle distribution, solidity, hub and shroud contour, meridional chord length, etc. Throughout this procedure, the aim of the designer is to obtain an optimum (i.e. light, reliable and robust) system with highest efficiency. Among optimization methods, zero order methods are reported to fit best for turbomachinery problems, due to their good performance in discrete and non-differentiable problems and their ability to find the global optimum. Genetic algorithm is the most widely used optimization method in turbomachinery optimization. Methods inspired by swarm intelligence are reported as promising global optimizers, whereas, to the author’s knowledge, there are no reported studies that employs such algorithms in turbomachinery throughflow optimization. These methods can find the neighborhood that provides the globally optimum design, rather than exactly finding the global design. This drawback is overcome by hybridizing genetic/swarm inspired algorithms by first order (gradient based) methods. Within this aspect, the present study focuses on developing genetic and swarm inspired algorithms hybridized with gradient based algorithms to find the optimum throughflow design of a transonic aero-engine fan module.

Phd Degree / Doktora

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