Makine Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263
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Article Citation - Scopus: 5Fused Filament Fabrication in Cad Education: a Closed-Loop Approach(Sage Publications inc, 2025) Totuk, Onat Halis; Selvi, Ozguen; Akar, SametIntegrating low-cost fused filament fabrication 3D printing as a foundation for learning 3D modelling is explored. This method blends traditional computer aided design (CAD) instruction with additive manufacturing possibilities. Experimental results demonstrate increased comprehension speed and reduced learning time. This hands-on approach empowers students by enabling direct engagement with the modelling process. Analogous to reverse engineering, the strategy instructs engineering students from final product to model creation, closing the gap between theory and practice. Incorporating 3D printing bridges this divide, enhancing understanding, creativity and problem-solving. The study underscores technology's influence on learning strategies, aligning with the surge of 3D printing in education. Results link advanced design technology usage to improved student performance, with 3D-printed materials yielding 45% higher grades and 30% faster task completion. This study advocates curricular advancement for design-focused careers through enhanced technology integration and favourable 3D printing model reception.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: 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: 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.Article Molecular dynamic approach to predict thermo-mechanical properties of poly(butylene terephthalate)/CaCO3 nanocomposites(2021) Seyedzavvar, Mirsadegh; Boğa, Cem; Akar, Samet; Pashmforoush, FarzadThermo-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. © 2021 Elsevier LtdConference Object Citation - WoS: 12Citation - Scopus: 16Investigation of Surface Roughness in Laser-Assisted Hard Turning of Aisi 4340(Elsevier, 2021) Sadeghi, Mohammad Hossein; Akar, Samet; Khatir, Farzad Ahmadi; Hossein Sadeghi, Mohammad; Ahmadi Khatir, FarzadIn recent years, new materials such as titanium, nickel alloys, and high-strength steels have been widely used in medical, nuclear, and other industries. Since the manufacturing of different components from these materials has always been associated with the machining process, the use of hard machining in their production is unavoidable. The short life of the cutting tool, the poor quality of the machined surfaces, and the long machining time are some of the challenging issues involved in the traditional machining of these materials. Therefore, researchers have investigated new machining techniques to increase the efficiency and quality of produced parts. Thermal-assisted machining, especially laser-assisted machining is one of the promising methods of machining difficult-to-machine materials. However, this process faces some challenges in terms of the achievable surface integrity of the machined surfaces. This research studies the effect of cutting and thermal parameters on the surface roughness in the laser-assisted turning (LAT) process of AISI 4340 hard steel with a hardness of 560 HV. The results illustrated that by selecting a proper combination of process parameters, the damage caused by the heat penetration into the workpiece can be minimized and the advantages of LAT can be benefited from. (C) 2020 The Authors. Published by Elsevier Ltd.