Yazar "Baskal, Tamer" seçeneğine göre listele

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    Application of Genetic Algorithm (GA) for Optimum Design of Module, Shaft Diameter and Bearing for Bevel Gearbox
    (Carl Hanser Verlag, 2012) Mendi, Faruk; Baskal, Tamer; Kulekci, Mustafa Kemal
    In this study selection of optimum module, shaft diameter and rolling bearing for conical gear has been done using genetic algorithm (GA). GA, is a novel stochastic method of optimization. GAs are based on the principles of natural selection and evolutionary theory. Objective function was optimized for the design variables between determined boundary values. The GA was constrained by taking into account the power, moment, velocity, wall thickness and bearing distances. Tooth strength and surface crush were considered to be design constraints for module optimization. The other algorithm constraints are maximum bending and torsion moments for shaft optimization, and working life for bearing optimization.
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    Öğe
    Application of Genetic Algorithms (GA) for the Optimization of Riveted Joints
    (Carl Hanser Verlag, 2013) Baskal, Tamer; Nursoy, Mustafa; Esme, Ugur; Kulekci, Mustafa Kemal
    Genetic algorithms have an effective search technique in a predefined research space based on natural selection theory. They use the same combination of selection, recombination, and mutation to evolve a solution to a problem. In this study, it has been demonstrated how to determine the optimal diameter, sheet thickness, and sheet width in riveted joints by means of genetic algorithms (GA). In this study the optimization of objective function for the variables in predetermined limit ranges was applied. Since the algorithms developed by this way are based on a principle to find out the best within a range like genetic process, it is becoming possible to predict the most suitable values for riveted junction dimensions in the defined limit range. The algorithms were restrained considering rivet diameter, thickness and width of the sheet, shear strength, tensile strength, and tear hazard of the sheet.
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    Öğe
    Optimization of Screw Elements by Genetic Algorithm
    (Carl Hanser Verlag, 2014) Baskal, Tamer
    In the present study, dimensional optimization of screw system elements, like shaft diameter, arm length and nut height, has been performed by tensile, torsional, and shear strength calculations via genetic algorithm. Genetic algorithm has been used to minimize the objective function work with the idea of natural selection. The volume of the designed screw device has been defined as the object function. The limit function is composed of the total stresses affecting the screw system. The algorithm was set up in this way, due to the fact that it relies on the principle of finding the best in a genetic process. Values of the variables within the limiting intervals ensure that the screw device has an optimum size.