Makine Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263
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Article Citation - Scopus: 2Effects of Earthquake Motion on Mechanism Operation: an Experimental Approach(Levrotto and Bella, 2015) Selvi, Ö.; Selvi, Özgün; Ceccarelli, M.; Aytar, E.B.; Makine MühendisliğiThis paper presents an experimental characterization of the effects of earthquakes on the operation of mechanical systems with the help of CaPaMan (Cassino Parallel Manipulator), which is a 3 DOF robot that can fairly well simulate 3D earthquake motion. The sensitivity of operation characteristics of machinery to earthquake disturbance is identified and characterized through experimental tests. Experimental tests have been carried out by using a slider-crank linkage, a small car model, and LARM Hand as test-bed mechanisms that have been sensored with proper acceleration or force sensors. Results are reported and discussed to describe the effects of earthquake motion on the characteristics of mechanism operation as a service application of the robotic CaPaMan system.Article Citation - WoS: 5Citation - Scopus: 6Prediction of the Heat Transfer Performance of Twisted Tape Inserts by Using Artificial Neural Networks(Korean Soc Mechanical Engineers, 2022) Kocak, Eyup; Ayli, EceA numerical study is undertaken to investigate the effect of twisted tape inserts on heat transfer. Twisted tapes with various aspect ratios and single, double, and triple inserts are placed inside a tube for Reynolds numbers ranging from 8000 to 12000. Numerical results show that the tube with a twisted tape and different numbers of tape is more effective than the smooth tube in terms of thermo-hydraulic performance. The highest heat transfer is achieved with the triple insert, with the highest turning number and an increment of 15 %. Then, an artificial neural network (ANN) model with a three-layer feedforward neural network is adopted to obtain the Nusselt number on the basis of four inputs for a heated tube with a twisted insert. Several configurations of the neural network are examined to optimize the number of neurons and to identify the most appropriate training algorithm. Finally, the best model is determined with one hidden layer and thirteen neurons in the layer. Bayesian regulation is chosen as the training algorithm. With the optimized algorithm, excellent precision for measuring the output is provided, with R2 = 0.97043. In addition, the optimized ANN architecture is applied to similar studies in the literature to predict the heat transfer performance of twisted tapes. The developed ANN architecture can predict the heat transfer enhancement performance of similar problems with R2 values higher than 0.93.Article Citation - Scopus: 3Numerical Simulation and Experimental Investigation: Metal Spinning Process of Stepped Thin-Walled Cylindrical Workpiece(Murat Yakar, 2022) Seyedzavvar, M.; Akar, S.; Abbasi, H.Many equipment and devices utilized in the aerospace industry are formed as symmetric parts through high plastic deformation of high strength sheet metal alloys with low thickness. Considering the inherent advantages of the spinning process of simple tooling and concentrated deformation loading, this process can be considered as one of the main options in producing these thin-sectioned lightweight parts. In this study, a Finite Element (FE) model has been developed to simulate the formation of a stepped thin-walled cylindrical workpiece of AISI 316 stainless steel alloy by spinning process. The FE simulation results were employed to investigate the effects of process parameters, including feed rate of the roller and rotational velocity of the mandrel on the distribution of stress and strain in the sheet metal, wrinkling failure, and thinning of the sheet metal during deformation. Experiments were carried out using selective input parameters based on the results of FE simulations. The comparison between FE simulations and experiments revealed that the developed model could predict the thinning of the sheet metals with over 93 % accuracy. Additionally, a good agreement between the experimentally deformed sheet configurations with those resulting from finite element simulations has been observed. © Author(s) 2022.Article Kinematic Analyses of Metallic Plate Perforation by Penetrators With Various Nose Geometries(defence Scientific information Documentation Centre, 2022) Akyurek, T.This study analyses kinematics of a metallic plate perforation by a penetrator with truncated ogive nose geometry to find solutions also to blunt, conical, ogive, and hemi-spherical nosed penetrators. Plugging, ductile hole enlargement, dishing, and petal forming failure modes are used in the analyses. Acceleration throughout perforation is calculated by using the related failure mode, analytical model, and the target-penetrator interaction geometry. Depending on the failure model; back lip and front lip formation during ductile hole enlargement, plug formation during plugging, and deflection of target plate during dishing is also analysed. Analyses are based on projectile???s equation of motion, momentum and energy equations, and projectile-target plate interactions. The analyses results for selected cases, with the impact velocity range 215-863 m/s, are compared with the test data. The residual velocity estimation for a strike velocity is close to the related test data with an error of 0.3-2.2 %, except for conical nosed penetrators at impact velocities approaching the ballistic limit velocity.Article Citation - WoS: 18Citation - Scopus: 19Effects of Electrolytic Copper and Copper Alloy Electrodes on Machining Performance in Electrical Discharge Machining (Edm)(Taylor & Francis inc, 2022) Esen, Ziya; Simsek, Ulke; Cogun, CanThe most important cost element of electric discharge machining (EDM) is the production of tool electrode (shortly electrode). In the EDM process, copper and its alloys are often used as electrode materials. The machining with EDM without increasing the costs can be achieved by selecting the proper electrode with low production and material costs as well as high workpiece material removal rate (MRR), low electrode wear rate (EWR), and relative wear (RW = MRR/EWR). In this study, the EDM performance outputs, namely, MRR and RW were experimentally investigated for electrolytic copper, CuCr1Zr (with and without aging treatment) and CuCo2Be alloy electrode materials for varying machining parameters. The performance outputs were affected by the electrode material and the applied aging treatment. The aged CuCr1Zr alloy electrodes had higher electrical conductivity and better machining performance than the as-received alloy. The CuCo2Be alloy electrodes exhibited moderate to high MRR; however, their RW was the highest. Although the electrolytic copper has moderate MRR performance compared to the investigated alloys, its low cost increased its performance index, making it a more suitable electrode material for EDM applications.Article Citation - WoS: 1Citation - Scopus: 1Analyses of Plate Perforation for Various Penetrator-Target Plate Combinations(Korean Soc Mechanical Engineers, 2022) Akyurek, TurgutIn this study, kinetics and kinematics of perforation process for various penetrator-target plate combinations is analyzed, a methodology in a flow chart format to decide on failure mode, and for each failure mode, an appropriate combined analytical model that requires only common test data is proposed. The proposed methodology and analytical models that are recommended for the related failure mode are assessed by using a huge amount of test data from the literature. The penetrator-target plate configurations cover the penetrators with ogive, conical, hemi-spherical and blunt noses, at different plate thicknesses, and plate thickness to penetrator diameter ratios, made of different metallic materials. Analyzed failure modes include ductile hole enlargement, plugging, dishing, and petal forming. Assessment is done for impact velocities ranging between 215-863 m/s. The estimations based on the proposed flow chart and recommended failure models are in good agreement with the related test data and numerical analysis results.Article Citation - WoS: 8Citation - Scopus: 8Supervised Learning Method for Prediction of Heat Transfer Characteristics of Nanofluids(Korean Soc Mechanical Engineers, 2023) Kocak, Eyup; Ayli, EceThis study focuses on the alication and investigation of the predictive ability of artificial intelligence in the numerical modelling of nanofluid flows. Numerical and experimental methods are powerful tools from an accuracy point of view, but they are also time- and cost-consuming methods. Therefore, using soft-computing techniques can improve such CFD drawbacks by patterning the CFD data. After obtaining the aropriate ANN and ANFIS architecture using the CFD data, many new data can be created without requiring numerical and experimental methods. In the scope of this research, the FCM-ANFIS and ANN methods are used to predict the thermal behaviour of the turbulent flow in a heated pipe with several nanoparticles. A parametric CFD study is carried out for water-TiO2, water-CuO, and water-SiO2 nanofluid through a pipe. The Reynolds number is varied between 7000 and 15000, and the nanofluid concentration is varied between 0.25 % and 4 %. The effects of using nanofluid on local values of Nusselt number and shear stress distribution were investigated. Numerical results indicate that with the increasing nanoparticle volume fraction of nanofluid, the average Nusselt number increases, but the required pumping power also increases. The obtained soft computing results demonstrate that the FCM clustering ANFIS has given better results both in training and testing when it is compared to the ANN architecture with an R-2 of 0.9983. Regarding this, the FCM-ANFIS is an excellent candidate for calculating the Nusselt number in heat transfer problems.Article Citation - WoS: 4Citation - Scopus: 4Performance Optimization of Finned Surfaces Based on the Experimental and Numerical Study(Asme, 2023) Ayli, Ece; Kocak, Eyup; Turkoglu, HasmetThis paper presents the findings of numerical and experimental investigations into the forced convection heat transfer from horizontal surfaces with straight rectangular fins at Reynolds numbers ranging from 23,600 to 150,000. A test setup was constructed to measure the heat transfer rate from a horizontal surface with a constant number of fins, fin width, and fin length under different flow conditions. Two-dimensional numerical analyses were performed to observe the heat transfer and flow behavior using a computer program developed based on the openfoam platform. The code developed was verified by comparing the numerical results with the experimental results. The effect of geometrical parameters on heat transfer coefficient and Nusselt number was investigated for different fin height and width ratios. Results showed that heat transfer can be increased by modifying the fin structure geometrical parameters. A correlation for Nusselt number was developed and presented for steady-state, turbulent flows over rectangular fin arrays, taking into account varying Prandtl number of fluids such as water liquid, water vapor, CO2, CH4, and air. The correlation developed predicts the Nusselt number with a relative root mean square error of 0.36%. This research provides valuable insights into the effects of varying Prandtl numbers on the efficiency of forced convection cooling and will help in the design and operation of cooling systems. This study is novel in its approach as it takes into account the effect of varying Prandtl numbers on the heat transfer coefficient and Nusselt number and provides a correlation for the same. It will serve as a valuable reference for engineers and designers while designing and operating cooling systems.Article Citation - WoS: 7Citation - Scopus: 10Combined Use of Ultrasonic-Assisted Drilling and Minimum Quantity Lubrication for Drilling of Niti Shape Memory Alloy(Taylor & Francis inc, 2023) Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, S. Engin; Yilmaz, Okan Deniz; Akar, SametThe drilling of shape-memory alloys based on nickel-titanium (Nitinol) is challenging due to their unique properties, such as high strength, high hardness and strong work hardening, which results in excessive tool wear and damage to the material. In this study, an attempt has been made to characterize the drillability of Nitinol by investigating the process/cooling interaction. Four different combinations of process/cooling have been studied as conventional drilling with flood cooling (CD-Wet) and with minimum quantity lubrication (CD-MQL), ultrasonic-assisted drilling with flood cooling (UAD-Wet) and with MQL (UAD-MQL). The drill bit wear, drilling forces, chip morphology and drilled hole quality are used as the performance measures. The results show that UAD conditions result in lower feed forces than CD conditions, with a 31.2% reduction in wet and a 15.3% reduction in MQL on average. The lowest feed forces are observed in UAD-Wet conditions due to better coolant penetration in the cutting zone. The UAD-Wet yielded the lowest tool wear, while CD-MQL exhibited the most severe. UAD demonstrated a & SIM;50% lower tool wear in the wet condition than CD and a 38.7% in the MQL condition. UAD is shown to outperform the CD process in terms of drilled-hole accuracy.Article Citation - WoS: 16Citation - Scopus: 17A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer(Asme, 2022) Ayli, Ece; Turkoglu, Hasmet; Kocak, EyupThe aim of this article is to introduce and discuss prediction power of the multiple regression technique, artificial neural network (ANN), and adaptive neuro-fuzzy interface system (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nanofluid flow in a pipe. Water and Al2O3 mixture is used as the nanofluid. Utilizing fluent software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm, and Reynolds number ranging between 7000 and 21,000. Based on the computationally obtained results, a correlation is developed for the Nusselt number using the multiple regression method. Also, based on the computational fluid dynamics results, different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) are tested to find the best ANN architecture. In addition, ANFIS is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor, and membership function (MF) type are optimized. The results obtained from multiple regression correlation, ANN, and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R-2 of 0.9987 for predictions.
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