Makine Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263
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Article Citation - WoS: 2Citation - Scopus: 1Exploring the Potential of Artificial Intelligence Tools in Enhancing the Performance of an Inline Pipe Turbine(Sage Publications Ltd, 2024) Celebioglu, Kutay; Ayli, Ece; Cetinturk, Huseyin; Tascioglu, Yigit; Aradag, SelinIn this study, investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe. The objective of the study is to determine the feasibility of implementing a turbine within a pipe and enhance its performance values within the operating range. The turbine within the pipe occupies significantly less space in hydroelectric power plants since a spiral casing is not used to distribute the flow to stationary vanes. Consequently, production and assembly costs can be reduced. Hence, there is a broad scope for application, particularly in small and medium-scale hydroelectric power plants. According to the results, the efficiency value increases on average by approximately 1.5% compared to conventional design, and it operates with higher efficiencies over a wider flow rate range. In the second part of the study, machine learning was employed for the efficiency prediction of an inline-type turbine. An appropriate Artificial Neural Network (ANN) architecture was initially obtained, with the Bayesian Regularization training algorithm proving to be the best approach for this type of problem. When the suitable ANN architecture was utilized, the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194. An R2 value of 0.99631 was achieved with the appropriate ANN architecture.Article Citation - WoS: 13Citation - Scopus: 14A Novel Cfd-Ann Approach for Plunger Valve Optimization: Cost-Effective Performance Enhancement(Elsevier Sci Ltd, 2024) Kaak, Abdul Rahman Sabra; Celebiog, Kutay; Bozkus, Zafer; Ulucak, Oguzhan; Ayli, Ece; Çelebioğlu, KutayThis paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD).Article Citation - WoS: 1Citation - Scopus: 1Performance Determination of Axial Wind Tunnel Fan With Reverse Engineering, Numerical and Experimental Methods(Asme, 2022) Ayli, Ece; Kocak, EyupIn today's technology, in case of the need for rehabilitation, renovation, or damage, it is necessary to recover the problems quickly with a cost-effective approach. In the case of destructive failure, or misdesign of the devices, replacing the problematic part with the new design is crucial. In order to substitute the related part with the efficient one, reverse engineering (RE) methodology is utilized. In this paper, from the perspective of engineering implementation and based on the idea of reverse engineering, axial wind tunnel fan is rehabilitated using numerical and experimental methods. The current study is focused on an axial pressurization fan placed into Cankaya University Mechanical Engineering Laboratory wind tunnel that has firm guaranteed specifications of 5.55 m(3)/s airflow capacity. The measurements performed during experiments showed that the fan provides less than 60% airflow compared with firm guaranteed specifications. In order to determine the problems of the existing fan, a reverse engineering methodology is developed, and the noncontact data acquisition method is used to form a computer aided drawing (CAD) model. A computational fluid dynamics (CFD) methodology is developed to analyze existing geometry numerically, and results are compared with an experimental study to verify numerical methodology. According to the results, the prediction accuracy of the numerical method can attain 92.95% and 96.38% for flowrate and efficiency, respectively, at the maximum error points.Article Citation - WoS: 11Citation - Scopus: 13Molecular Dynamic Approach To Predict Thermo-Mechanical Properties of Poly(Butylene Terephthalate)/Caco3 Nanocomposites(Elsevier, 2021) Boga, Cem; Akar, Samet; Pashmforoush, Farzad; Seyedzavvar, MirsadeghThermo-mechanical properties of poly(butylene terephthalate) polymer reinforced with carbonate calcium nanoparticles have been investigated using molecular dynamics simulations. Detailed analyses have been conducted on the effects of nanofiller content, at concentration levels of 0-7 wt%, on the mechanical properties of PBT, i.e. Young's modulus, Poisson's ratio and shear modulus. Thermal properties, including thermal conductivity and glass transition temperature, have been determined using Perl scripts developed based on nonequilibrium molecular dynamics and a high temperature annealing procedure, respectively. Experiments have been performed to verify the accuracy of the results of MD simulations. The CaCO3/PBT nanocomposites were synthesized using melt blending and mold injection techniques. The uniaxial tensile test, thermal conductivity, differential scanning calorimetry and x-ray diffraction spectroscopy measurements were conducted to quantify the thermo-mechanical properties of such nanocomposites experimentally. The results showed significant improvements in the mechanical properties by addition of CaCO3 nanoparticles due to strong binding between rigid particles and PBT polymer and high nucleation effects of nanoparticles on the matrix. Thermal conductivity and glass transition temperature of nanocomposites represented a consistent increase with the ratio of CaCO3 nanoparticles up to 5 wt% with an enhancement of 38% and 36% with respect to that of pure PBT, respectively.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: 1Prediction of the Onset of Shear Localization Based on Machine Learning(Cambridge Univ Press, 2023) Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk; Akar, Samet; Ayll, EcePredicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.Article Citation - WoS: 6Citation - Scopus: 7Machine Learning Based Developing Flow Control Technique Over Circular Cylinders(Asme, 2023) Turkoglu, Hasmet; Ayli, Ece; Kocak, EyupThis paper demonstrates the feasibility of blowing and suction for flow control based on the computational fluid dynamics (CFD) simulations at a low Reynolds number flows. The effects of blowing and suction position, and the blowing and suction mass flowrate, and on the flow control are presented in this paper. The optimal conditions for suppressing the wake of the cylinder are investigated by examining the flow separation and the near wake region; analyzing the aerodynamic force (lift and drag) fluctuations using the fast Fourier transform (FFT) to separate the effects of small-scale turbulent structures in the wake region. A method for stochastic analysis using machine learning techniques is proposed. Three different novel machine learning methods were applied to CFD results to predict the variation in drag coefficient due to the vortex shedding. Although, the prediction power of all the methods utilized is in the acceptable accuracy range, the Gaussian process regression (GPR) method is more accurate with an R-2(coefficient of determination) > 0.95. The results indicate that by optimizing the blowing and suction parameters like mass flowrate, slot location, and the slot configuration, up to 20% reduction can be achieved in the drag coefficient.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: 7Citation - Scopus: 7A Study on the Μwire-Edm of Ni55.8ti Shape Memory Superalloy: an Experimental Investigation and a Hybrid Ann/Pso Approach for Optimization(Springer Heidelberg, 2023) Seyedzavvar, Mirsadegh; Boga, Cem; Akar, SametThe unique properties of high hardness, toughness, strain hardening, and development of strain-induced martensite of nickel-titanium superalloys made the micro-wire electro discharge machining (mu wire-EDM) process one of the main practical options to cut such alloys in micro-scale. This paper presents the results of a comprehensive study to address the response variables of Ni55.8Ti superalloy in mu wire-EDM process, including the kerf width (KW), material removal rate (MRR), arithmetic mean surface roughness (R-a) and white layer thickness (WLT). To this aim, the effects of pulse on-time (T-on), pulse off-time (T-off), discharge current (I-d) and servo voltage (SV) as input parameters were investigated using the experiments conducted based on Taguchi L-27 orthogonal array. The results were employed in the analysis of variance (ANOVA) to examine the significance of input parameters and their interactions with the output variables. An optimization approach was adopted based on a hybrid neural network/particle swarm optimization (ANN/PSO) technique. The ANN was employed to achieve the models representing the correlation between the input parameters and output variables of the mu wire-EDM process. The weight and bias factor matrices were obtained by ANN in MATLAB and together with the feed forward/backpropagation model and developed functions based on PSO methodology were used to optimize the input parameters to achieve the minimum quantities of KW, R-a and WLT and the maximum value of MRR, individually and in an accumulative approach. The results represented a maximum accumulative error of nearly 8% that indicated the precision of the developed model and the reliability of the optimization approach. At the optimized level of input parameters obtained through the accumulative optimization approach, the KW, R-a, and WLT remained nearly intact as compared with the levels of responses obtained in the individual optimization approach, while there was a sacrifice in the machining efficiency and reduction in the MRR in the mu wire-EDM process of Nitinol superalloy.Article Citation - WoS: 7Citation - Scopus: 7Prediction of White Layer Formation in Μ-Wedm Process of Niti Shape Memory Superalloy: Fem With Experimental Verification(Springer London Ltd, 2021) Akar, Samet; Meshri, Hassan Ali M.; Seyedzavvar, Mirsadegh; Ilkhchi, Reza NajatiMicroscopic changes in the surface of nickel-titanium (nitinol) shape memory alloys (SMAs) in micro-wire electro-discharge machining (mu-WEDM) due to the formation of a resolidified layer on the machined surface, called white layer, are one of the main drawbacks in the processing of such alloys. Since these changes significantly affect the shape memory and elastic recovery characteristics of these alloys, reduction of the white layer thickness (WLT) based on the selection of optimum process parameters is essential to raise the quality of the machined parts. In this regard, a finite element model (FEM) has been developed to simulate the effects of mu-WEDM process parameters, including discharge current, pulse on-time, pulse off-time, and servo voltage, on the heat distributing in Ni55.8Ti SMA to predict the WLT. The flushing efficiency of electric discharges and the effect of flow regime of the dielectric fluid on the heat distribution in the workpiece and the formation of the WLT are analyzed. Experimental data are used to verify the accuracy of the FEM. The results show that the developed model can predict the WLT in mu-WEDM process of Ni55.8Ti SMA with an average error of 14%. The effects of discharge parameters on the formation of the WLT are discussed in details based on the results of the FEM.
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