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Öğe 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 KemalIn 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.Öğe Application of Genetic Algorithms (GA) for the Optimization of Riveted Joints(Carl Hanser Verlag, 2013) Baskal, Tamer; Nursoy, Mustafa; Esme, Ugur; Kulekci, Mustafa KemalGenetic 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.