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    Öğe
    Dimensional accuracy analysis of samples printed in delta and cartesian kinematic three dimensional printers
    (Gazi Üniversitesi, 2021) İncekar, Erkan; Kaygısz, 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.
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    Öğe
    Estimation of Blood Calcium and Potassium Values from ECG Records
    (Sciendo, 2024) Babur, Sebahattin; Moghaddamnia, Sanam; Bozkurt, Mehmet Recep
    The identification of diseases caused by changes in ion concentration is quite difficult and yet plays a decisive role in the success of clinical care, diagnosis and treatment. The clinically proven approach to diagnosing electrolyte concentration imbalance is blood tests. There is a need to provide a non-invasive diagnostic method that is not of a temporary nature. Bio-signals such as the electrocardiogram (ECG) can be used to meet this demand and become diagnostic tools that facilitate home monitoring of electrolyte concentration on a permanent basis. This study investigates the feasibility and efficiency of methods based on machine learning (ML) and ECG recordings in monitoring critical levels of existing potassium and calcium concentration. Morphological, frequency and frequency-time domain features were extracted to automatically estimate calcium and potassium levels. Furthermore, the potential of estimates based on modeling approaches will be demonstrated to gain insights into relevant clinical findings and improve the performance of monitoring approaches. Using the hold-out validation method, the best results in terms of mean square error (MSE) and R for estimating the calcium value are 0.7157 and 0.57347, using fuzzy inference systems (FIS). Here, R represents the proportion of the variance in the calcium value that is explained by the model.