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    Dimensional accuracy analysis of samples printed in delta and cartesian kinematic three dimensional printers
    (Gazi University, 2021) İncekar, Erkan; Kaygısız, Hüseyin; Babur, Sebahattin
    The motion mechanisms of manufacturing and robotic systems are developed in different structures, mainly in cartesian and delta structures having series or parallel movement abilities according to the capacity and construction structure of the system. Different systems are used according to the criteria such as bearing load capacity, sensitivity or cost of the system. In this study, the performances of machines installed in the delta and cartesian kinematic structures, which are mostly used in the kinematic systems of three - dimensional printers, were analyzed. In this context, in two different machines with these two construction structures, the same boundary conditions and 4 pieces of calibration parts especially in manufacturing features were printed. 23 different elements that constituted the calibration part were measured, tabulated, statistically analyzed, and the acceptable measuremental tolerance ranges of the elements were determined and the accuracy values of the machines were compared. As a result of this study, according to T test results, 15 of the 23 measurements on the Cartesian system based three-dimensional printers were obtained as acceptable in terms of tolerance range as well as 9 of the 23 different measurements were obtained as acceptable on Delta system. Consequently, operation accuracy of the Cartesian system based three-dimensional printers were higher than the Delta system under the same working conditions and manufacturing parameters.
  • Küçük Resim Yok
    Öğe
    Machine Learning Based Performance Development for Diagnosis of Breast Cancer
    (IEEE, 2015) Bektas, Burcu; Babur, Sebahattin
    Breast cancer is prevalent among women and develops from breast tissue. Early diagnosis and accurate treatment is vital to increase the rate of survival. Identification of genetic factors with microarray technology can make significant contributions to diagnosis and treatment process. In this study, several machine learning algorithms are used for Diagnosis of Breast Cancer and their classification performances are compared with each other. In addition, the active genes in breast cancer are identified by attribute selection methods and the conducted study show success rate 90,72 % with 139 feature.