Yazarlar
- Yaylacı, Murat(5)
- Uzun Yaylacı, Ecren(3)
- Öner, Erdal(3)
- Oktay, Mine Gül(2)
- Turan, Muhittin(2)
- Yaylacı, Ecren Uzun(2)
- Özdemir, Mehmet Emin(2)
- Abushattal, Ahmad(1)
- Akçadirci, Esra(1)
- Ay, Sevil(1)
- Birinci, Ahmet(1)
- Can, Zehra(1)
- Colak, M.(1)
- Gurgen, Ayşenur(1)
- Güneş, Sibel(1)
- Ipek, M.(1)
- Kara, Yakup(1)
- Kiraz, A.(1)
- Manay, Eyüphan(1)
- Mirzaloğlu, İrem(1)
- Serencam, Huseyin(1)
- Teke, C.(1)
- Teke, Çağatay(1)
- Terzi, Merve(1)
- Ylldlz, Sibel(1)
- Çakir, Ǔur(1)
- Çomakli, Ömer(1)
- Özceyhan, Veysel(1)
- Öztekin, Ertekin(1)
- Öztürk, Şevval(1)
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ2A Multi-perspective Exploration of Contact Behavior in Orthotropic Layer Resting on Isotropic Half-Plane
Terzi, Merve; Öner, Erdal; Uzun Yaylacı, Ecren; Oktay, Mine Gül; Yaylacı, Murat
Contact problems which concern the stress distribution resulting from the interaction between two elastic bodies have long been a focus of research. In the field of solid mechanics, they hold particular importance because loads are generally transmitted to deformable bodies through their points of contact. This study investigates the contact problem of a layered medium containing a homogeneous orthotropic layer and an isotropic half-plane using a multi-method approach. A rigid flat indenter is applied to the orthotropic layer with frictional effects neglected, while the influence of body forces in the orthotropic layer is taken into account. Key contact parameters such as the contact stress under the indenter, the initial separation load, and the point of initial separation are determined as part of the analysis. The study is structured into three main sections. In the first section, the problem is analytically examined based on classical elasticity theory. Within this framework, a singular integral equation governing the contact problem is derived and solved numerically. The second section presents a finite element analysis using ANSYS software to simulate the contact interaction and evaluate the stress distribution. In the third and final section, the problem is addressed through artificial neural network (ANN) techniques, offering a data-driven perspective. Ultimately, the results obtained from the analytical, numerical (FEM), and ANN-based methods are compared, showing excellent agreement across all approaches.
AtıfScopus15WoS10OpenAlex16
İncele - MakaleAçık Erişim2025Scopus SJRQ2Tri-method analysis of contact mechanics in orthotropic-isotropic materials
Öner, Erdal; Oktay, Mine Gül; Uzun Yaylacı, Ecren; Yaylacı, Murat; Mirzaloğlu, İrem
This study examines the contact mechanics of a homogeneous orthotropic layer resting on a homogeneous isotropic half-space without being bonded, subjected to loading by a rigid cylindrical punch. The effect of the orthotropic layer’s body force has been taken into account in the study. The study is conducted in three phases. In the first phase, the contact problem is analytically tackled using advanced methods such as elasticity theory, integral transform techniques, and Gauss–Chebyshev integration. The second phase utilizes finite element analysis through ANSYS software, accurately modeling the system. In the final phase, an artificial neural network is employed, allowing the system to learn and recognize intricate patterns in the data. The standout feature of this study is its thorough comparison of these three distinct methodologies, offering a comprehensive understanding of the contact mechanics between isotropic and orthotropic materials. The results reveal key insights into contact length, maximum contact stress, critical separation load, and separation distance, all as functions of critical dimensionless parameters. This study is significant in today’s advancing field of contact mechanics as it not only explores the combined impact of body forces and the interaction between orthotropic and isotropic materials but also uniquely compares the results using three distinct methods, offering comprehensive insights that address both theoretical and practical challenges.
AtıfScopus27WoS27OpenAlex22İncele - MakaleYalnız künye2024Scopus SJRQ1Research of the crack problem of a functionally graded layer
Yaylacı, Murat; Yaylacı, Ecren Uzun; Turan, Muhittin; Özdemir, Mehmet Emin; Öztürk, Şevval; Ay, Sevil
In this study, the two-dimensional crack problem was investigated by using the finite element method (FEM)based ANSYS package program and the artificial neural network (ANN)-based multilayer perceptron (MLP) method. For this purpose, a half-infinite functionally graded (FG) layer with a crack pressed through two rigid blocks was analyzed using FEM and ANN. Mass forces and friction were neglected in the solution. To control the validity of the crack problem model exercised, the acquired results were compared with a study in the literature. In addition, FEM and ANN results were checked using Root Mean Square Error (RMSE) and coefficient of determination (R2), and a well agreement was found. Numerical solutions were made considering different geometric parameters and material properties. The stress intensity factor (SIF) was examined for these values, and the results were presented. Consequently, it is concluded that the considered non-dimensional quantities have a noteworthy influence on the SIF. Also FEM and ANN can be logical alternative methods to time-consuming analytical solutions if used correctly.
AtıfScopus27WoS30OpenAlex6İncele - MakaleYalnız künye2023Scopus SJRQ2Free vibration and buckling of functionally graded porous beams using analytical, finite element, and artificial neural network methods
Turan, Muhittin; Uzun Yaylacı, Ecren; Yaylacı, Murat
In this study, an analytical solution based on the first-order shear deformation theory was performed for free vibration and buckling analysis of functionally graded porous beams (FGM-P) subjected to various boundary conditions. Also, this problem is solved by using finite element (FEM) and artificial neural network (ANN) methods. Here a Ritz-based analytical solution is used, and different polynomial series functions are proposed for each boundary condition. Lagrange's principle was used while deriving the equations of motion. A power-law rule describes the variation of the beam's materials in volume. The normalized fundamental frequencies and critical buckling loads are obtained for various boundary conditions, power-law index (k), slenderness (L/h), porosity coefficient (e), and porosity distribution (FGM-P1, FGM-P2). The polynomial series functions used in this study were verified with the literature, and the numerical results obtained were compared with FEM and ANN. The results obtained are quite compatible with each other.
AtıfScopus140WoS136OpenAlex101İncele - MakaleAçık Erişim2022Scopus SJRQ2Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach
Teke, Çağatay
Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R2) value of 0.986 confirms the high prediction performance of the model.
AtıfScopus4WoS4İncele - MakaleYalnız künye2022Scopus SJRQ2Application of artificial neural networks in the analysis of the continuous contact problem
Yaylacı, Ecren Uzun; Öner, Erdal; Yaylacı, Murat; Özdemir, Mehmet Emin; Abushattal, Ahmad; Birinci, Ahmet
This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for contact pressures and contact lengths under the rigid punch, the initial separation loads, and the initial separation distances of a contact problem. The problem consisted of two elastic infinitely layers (EL) loaded by means of a rigid cylindrical punch and resting on a half-infinite plane (HP). Firstly, the problem was formulated and solved theoretically using the Theory of Elasticity (ET). Secondly, the contact problem was extended based on the ANN. External load, the radius of punch, layer heights, and material properties were created by giving examples of different values used at the training and test stages of ANN. Finally, the accuracy of the trained neural networks for the case was tested using 134 new data, generated via ET solutions to determine the best network model. ANN results were compared with ET results, and well agreements were achieved.
AtıfScopus62WoS60OpenAlex13İncele - MakaleAçık Erişim2021Scopus SJRQ3Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm
Gurgen, Ayşenur; Serencam, Huseyin; Kara, Yakup; Can, Zehra; Ylldlz, Sibel
In this study, the optimum values of propolis ethanol extracts parameters were determined with the use of single and multi-objective optimization procedures. The euclidean distance approach was used in the multi-objective optimization process. Firstly, propolis was extracted using water with ethanol contents 40, 50, 60, 70 and 80% for 8, 10, 12, 16, 20 and 24 h. Then, total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) activities of all extracts were determined. With the obtained data a prediction model was produced with the use of artificial neural networks (ANN), and optimization was performed using a moth-flame (MFO) algorithm. The best prediction models for the TPC and FRAP were observed in 2-5-1 and 2-5-1 network architecture with the mean absolute percentage error (MAPE) values, 5.126 and 2.451%, respectively. For maximum TPC, the extraction parameters were determined as ethanol content 57.5% and extraction time 13.56 h. To maximize FRAP, the optimized extraction parameters were ethanol content 72.03% and extraction time 18.04 h. The optimum extraction conditions for both maximum values of the studied assays were ethanol content 70.03% and extraction time 16.93 h. The study concluded that the integrated ANN and MFO algorithm system can be used in single and multi-objective optimization of extraction parameters. The established optimization model can save time, money, labor and energy.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2019Scopus SJRQ2Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network
Teke, C.; Colak, M.; Kiraz, A.; Ipek, M.
Casting simulation programs are computer programs that digitally model the casting of an alloy in the sand, shell, or permanent mold and, then, the cooling and solidification processes. However, obtaining consistent results out of casting modeling depends on the incorporation of many accurate parameters and boundary conditions. Critical Fraction of Solid (CFS), which is one of the most important of these parameters, is defined as the point where solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies based on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process, carried out using low-pressure die casting method, is predicted by using Artificial Neural Network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, and pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low-pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values are obtained in this validation range of 33% to 61%. These CFS values are used in the development of ANN models. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE) are used to assess the prediction accuracy of the ANN models. Calculated values of MAE, MAPE, and MSE are 0.0188, 7.06%, and 0.0006, respectively. The results show that the proposed ANN model predicts a CFS value with high accuracy.
AtıfScopus6WoS6OpenAlex4İncele - MakaleYalnız künye2016Scopus SJRQ2ANN based investigations of reliabilities of the models for concrete under triaxial compression
Öztekin, Ertekin
Purpose - A lot of triaxial compressive models for different concrete types and different concrete strength classes were proposed to be used in structural analyses. The existence of so many models creates conflicts and confusions during the selection of the models. In this study, reliability analyses were carried out to prevent such conflicts and confusions and to determine the most reliable model for normal- and high-strength concrete (NSC and HSC) under combined triaxial compressions. The paper aims to discuss these issues. Design/methodology/approach - An analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. After verifying the validity of the model by making comparisons with the models in the literature, reliabilities of all models were investigated. The Monte Carlo simulation method was used in the reliability studies. Artificial experimental data required for the Monte Carlo simulation method were generated by using artificial neural networks. Findings - The validity of the proposed model was verified. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Finally, the reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Research limitations/implications - Concrete compressive strength and lateral compression were taken as variables in the model. Practical implications - The reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Originality/value - A new analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Artificial experimental data were obtained by using artificial neural networks. Four different artificial neural networks were developed to generate artificial experimental data. They can also be used in the estimations of the strength of NSC and HSC under different triaxial loadings.
AtıfScopus5WoS5OpenAlex6İncele - MakaleYalnız künye2012Scopus SJRQ3The prediction of heat transfer and fluid characteristics for equilateral triangular bodies in tandem arrangement by artificial neural networks
Manay, Eyüphan; Güneş, Sibel; Akçadirci, Esra; Özceyhan, Veysel; Çakir, Ǔur; Çomakli, Ömer
The objective of this study is to investigate the effect of the spacing between equilateral dual triangular bodies symmetrically placed into the channel axis under steady state conditions on heat transfer and fluid characteristics by using artificial neural networks (ANN). The Back Propagation (BP) training algorithm was applied to train the model. The successful application proved that ANN model can be used for predicting the Nusselt number and skin friction coefficient as a convenient and effective method. The distribution of local Nusselt number, skin friction coefficient along the channel wall and overall enhancement ratio of all investigated cases are presented.
AtıfScopus5İncele