Today, 3D manufacturing technologies are shown as candidates to replace traditional manufacturing technologies. In this direction, many studies are carried out to reduce the disadvantages of 3D manufacturing technologies. The first few of these disadvantages are; high production cost, slow production speed, and lower strength values of the produced product compared to traditional methods. Increasing or decreasing the printing speed, which is one of the 3d production parameters, appears as a parameter that will directly affect the strength and production costs of the produced product. For this reason, it is important to determine the effects that may occur on the mechanical properties of the product produced by changing the printing speed in terms of choosing the printing speed according to the intended use of the product. In this study, the effect of desktop Fused Deposition Modelling (FDM) 3D printing speed on mechanical properties was investigated. Tensile test samples were produced using Polylactic Acid (PLA) material at seven different printing speeds using two different 3D printers operated without bed heating. The mass, hardness, surface roughness, and porosity values of the produced samples were determined. Fractured surfaces of the samples were analyzed using Scanning electron microscopy (SEM) images. The results show that an increase in the printing speed decreases the mass, the top surface hardness, and the tensile strength and increases the porosity, the arithmetic average roughness of the products produced with both 3D printers.

Bu çalışmada, Ultimaker 2 Extended 3 boyutu yazıcı ile farklı yazdırma hızlarında (20-40-60-80-100-120-140mm/s), ABS (acrylonitrile butadiene styrene) ve PLA (poly-lactic acid) malzeme kullanılarak çekme test numuneleri üretilmiştir. Yazdırma hızının, mekanik özellikler üzerindeki etkisi araştırılmıştır. Üretilen numunelerin kütleleri, sertlikleri ve yüzey pürüzlülükleri ölçülmüş, porozite değerleri hesaplanmış ve üretilen numunelere çekme testi yapılmıştır. Çekme testi sonrasında kopmuş numunelerin kopma bölgelerinden taramalı elektron mikroskobu (TEM) ile görüntüler alınmıştır. Her iki malzeme ile aynı parametrelerde üretilen numunelerin mekanik özellikleri kıyaslanmıştır. Sonuç olarak; PLA malzeme ile üretilen numunelerin çekme mukavemetlerinin ABS malzeme ile üretilen numunelerden daha yüksek olduğu, ABS malzeme ile üretilen numunelerin kopma uzaması değerlerinin de PLA malzeme ile üretilen numunelerden daha yüksek olduğu tespit edilmiştir.

In this presented study, the cooling problem of the I.T.U. Triga Mark-II reactor has been handled and analyzed, and solutions were proposed. First of all, a thermal model of the reactor, heat exchanger, and cooling tower trio was established in the reactor. With this model, which was obtained with the help of experimental data, the parameters affecting the change of reactor water temperature over time were determined, and significant findings were obtained by investigating the possibilities of increasing the cooling power of the existing system. Then, using these mathematical equations, the effects of parameters that can affect the power of the reactor cooling system are investigated. The parameters affecting the cooling power are the cooling water flow rates in the second cooling circuits and the deposited layer that may exist as a result of numerical calculations. Different models have been created with machine learning algorithms (page regression, decision tree) to estimate the effect of the deposit layer. The mathematical and predictive models obtained with the experimental data for the heat transfer coefficient of the deposit layer, hbd, were compared. The pace regression algorithm modeled the hbd values with the least error rate (RMSE: 1.66) among the models. It has been calculated that the average tank water temperature will decrease by approximately 3.5?C if the deposits layer is cleared.

Tr en İnsansı robotların tasarım sürecinde, klasik yöntemler ile yürüme hareket denklemlerinin çıkarılması ve bu denklemlerin neticesinde yürüme uzuvlarını tahrik edecek büyüklüğün elde edilmesi oldukça zahmetli ve zaman isteyen bir süreçtir. İnsanın yürüyebilmesi için ayak uzvunu oluşturan her bir parçanın bir kas grubunun tahrik edilmesi gerekmektedir. Bu kas grupları yerine robotlarda actuatorler, servo motorlar, mekanizmalar gibi tahrik elemanı kullanılmakta ve bu tahrik elemanın seçimi ve şiddeti oldukça önem taşımaktadır. Bu makale de 2R manipülatör mekanizmasının ile insansı bir robotun bacak uzvu olarak kullanılmış ve yürüme yörüngesini veren denklemler oluşturularak insana en yakın yürüme modelini “Working Model 2D” adlı simülasyon programında elde edilmesi amaçlanmaktadır. For a person to walk, each piece forming a foot should be driven by a muscle group. Instead of these muscle groups, robots use actuators such as actuators, servo motors, and mechanisms. For this reason, the selection and the severity of this driving element are very important. In this thesis study, a humanoid robot is used as a limb with 2R manipulator mechanism and it is aimed to obtain the closest walking model to the human being in the simulation program called "Working Model 2D" by creating equations giving walking orbit. 

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining.