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Öğe Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials(Hindawi Ltd, 2018) Bozdemir, MustafaTo know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is also developed with the obtained results from the experiments. Humidity condition, tool type, cutting speed, cutting rate, and depth of cutting parameters were used as input and average surface roughness value were used as output of the ANN model. Testing results showed that ANN can be used for prediction of average surface roughness.Öğe The Effects of Humidity on Cast PA6G during Turning and Milling Machining(Hindawi Ltd, 2017) Bozdemir, MustafaWe compared the foundry PA6G samples in several dry and humid but different storage environments by processing them under the same cutting conditions such as progress rate (100, 120, 140, and 160 mm/min), cutting rate (90, 110, and 130 m/min), and cutting depth (1, 1.5, 2, 2.5, and 3 mm), in terms of formation of average surface roughness values. An improvement of 10.4% in average surface roughness was observed in the measurements performed after the milling process on the humid material and then the process was carried out under a dry condition. Degradation of about 14% in the average surface roughness was observed. The measurement was carried out after the samples were used inmilling measurement which was performed on the dry PA6G material that was kept in a humid environment. An improvement of 6.4% in average surface roughness was observed. The measurements were performed after CNC machines process was applied on humid and dried PA6G material. This difference between milling and turning procedures is caused by the workpiece losing its humidity in the turning machine due to the turning effect. It was noted that the processes performed on the CNC turning stand were less affected by the humidity factor.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.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.
