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Öğe AISI 304 ve AISI 316 Östenitik Paslanmaz Çeliklerin İşlenebilirliğinin Değerlendirilmesi(2017) Özbek, Nursel Altan; Çiçek, Adem; Gülesin, Mahmut; Özbek, OnurBu çalışmada AISI 304 ve AISI 316 östenitik paslanmaz çeliklerin işlenebilirliğini değerlendirmek amacıyla kaplamasız tungsten karbür kesici takımlar kullanılarak tornalama deneyleri yapılmıştır. Deneyler, kuru kesme şartlarında sabit kesme derinliği (2,4 mm), dört kesme hızı (100, 120, 140 ve 160 m/dak) ve üç ilerleme hızında (0,15, 0,3 ve 0,45 mm/dev) gerçekleştirilmiştir. AISI 304 ve AISI 316 çeliklerin işlenebilirliği takım aşınması, esas kesme kuvveti ve yüzey pürüzlülüğü açısından değerlendirilmiştir. Deney sonuçları AISI 316 çeliğinin işlenmesinde kesici takımın daha fazla aşındığını göstermiştir. Ayrıca AISI 316 çeliğinde daha yüksek kesme kuvveti ve yüzey pürüzlülüğü değerleri ölçülmüştür. Deneysel çalışma sonunda, AISI 316 çeliğinin AISI 304 çeliğine kıyasla işlenebilirliğinin zor olduğu ortaya konulmuşturÖğe AISI 304 ve AISI 316 Östenitik Paslanmaz Çeliklerin İşlenebilirliğinin Değerlendirilmesi(Gazi Univ, 2017) Özbek, Nursel Altan; Çiçek, Adem; Gülesin, Mahmut; Özbek, OnurIn this study, turning tests were performed to assess machinability of AISI 304 and AISI 316 austenitic stainless steels using uncoated tungsten carbide tools. The tests were conducted at four cutting speeds (100, 120, 140 and 160 m/min), three feed rates (0.15, 0.3 and 0.45 mm/rev), and a fixed depth of cut (2.4 mm) under dry cutting conditions. Machinability of AISI 304 and AISI 316 steels was evaluated in terms of tool wear, main cutting force and surface roughness. Experimental results showed that larger wear damages formed on the cutting inserts used for machining of AISI 316 steel. In addition, higher values of cutting forces and surface roughness were measured in machining of AISI 316 steel. In consequence of experimental study, it was found that AISI 316 steel had harder machinability characteristics than AISI 304 steel.Öğe ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel(Springer, 2015) Kara, Fuat; Aslantaş, Kubilay; Çiçek, AdemIn this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN + Al2O3 + TiN and Al2O3), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (F (c)) and the feed force (F (f)) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for F (c), the optimal results for F (f) were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.Öğe ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel(Springer London Ltd, 2013) Akıncıoğlu, Sıtkı; Mendi, Faruk; Çiçek, Adem; Akıncıoğlu, GülşahThis paper focuses on artificial neural network (ANN)-based modeling of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. A number of drilling experiments were conducted at all combinations of different cutting speeds (50, 55, 60, and 65 m/min) and feed rates (0.063 and 0.08 mm/rev) to obtain training and testing data. The experimental results showed that the surface roughness (Ra) and roundness error (Re) values were obtained with the TiN monolayer- and TiAlN/TiN multilayer-coated drills, respectively. Using some of the experimental data in training stage, an ANN model was developed. To evaluate the performance of the developed ANN model, ANN predictions were compared with the experimental results. It was found that the determination coefficient values are more than 0.99 for both training and test data. Root mean square error and mean error percentage values were very low. ANN results showed that ANN can be used as an effective modeling technique in accurate prediction of the Ra and Re.Öğe Application of Deep Cryogenic Treatment to Uncoated Tungsten Carbide Inserts in the Turning of AISI 304 Stainless Steel(Springer, 2016) Özbek, Nurse Altan; Çiçek, Adem; Gülesin, Mahmut; Özbek, OnurThis study investigated the effects of deep cryogenic treatment (DCT) on the wear performance of uncoated tungsten carbide inserts. AISI 304 austenitic stainless steel, widely used in industry, was selected as the workpiece material. Cutting experiments showed that the amount of wear significantly increased with increasing cutting speed. In addition, it was found that DCT contributed to the wear resistance of the turning inserts. The treated turning inserts were less worn by 48 and 38 pct in terms of crater wear and notch wear, respectively, whereas they exhibited up to 18 pct superior wear performance in terms of flank wear. This was attributed to the precipitation of new and finer eta-carbides and their homogeneous distribution in the microstructure of the tungsten carbide material after deep cryogenic treatment. Analyses via image processing, hardness measurements, and SEM observations confirmed these findings.Öğe Application of Taguchi Method for Surface Roughness and Roundness Error in Drilling of AISI 316 Stainless Steel(Assoc Mechanical Engineers Technicians Slovenia, 2012) Çiçek, Adem; Kıvak, Turgay; Samtaş, GürcanIn this study, the effects of deep cryogenic treatment and drilling parameters on surface roughness and roundness error were investigated in drilling of AISI 316 austenitic stainless steel with M35 HSS twist drills. In addition, optimal control factors for the hole quality were determined by using Taguchi technique. Two cutting tools, cutting speeds and feed rates were considered as control factors, and L-8(2(3)) orthogonal array was determined for experimental trials. Multiple regression analysis was employed to derive the predictive equations of the surface roughness and roundness error achieved via experimental design. Minimum surface roughness and roundness error were obtained with treated drills at 14 m/min cutting speed and 0.08 mm/rev feed rate. Confirmation experiments showed that Taguchi method precisely optimized the drilling parameters in drilling of stainless steel.Öğe Artificial neural network based modelling of performance of a beta-type Stirling engine(Sage Publications Ltd, 2013) Özgören, Yaşar Önder; Çetinkaya, Selim; Sarıdemir, Suat; Çiçek, Adem; Kara, FuatIn this article, artificial neural network has been used in order to predict the power (P) and torque (T) values obtained from a beta-type Stirling engine that uses air as working fluid. Experimental data have been obtained for different charge pressures and hot source temperatures using ZrO2-coated and uncoated displacers. The closest artificial neural network results to experimental torque and power values were obtained with double hidden layer 5-13-9-1 and 5-13-7-1 network architectures, respectively. The best prediction values were obtained by Levenberg-Marquardt learning algorithm. Correlation coefficient (R-2) for the torque values were 0.998331 and 0.997231 for the training and test sets, respectively, while R-2 value for power values were 0.998331 and 0.997231 for the training and test sets, respectively. R-2 values show that the developed artificial neural network is an acceptable and powerful modelling technique in predicting the torque and power values of the beta-type Stirling engine.Öğe BDT ortamında farklı ölçeklerde tasarlanan katı modellerin ölçekleme değerlendirmesi(2008) Çiçek, AdemBu çalışmada, (Bilgisayar Destekli Tasarım) BDT ortamında farklı ölçeklerde tasarlanan katı modeller arasında ölçekleme oranını tespit etmek için bir yaklaşım geliştirilmiştir. Yaklaşıma girdi olarak katı modellerin STEP "Standard for the Exchange of Product Data Model" veri dönüşüm formatı kullanılmıştır. BDT ortamında tasarlanan katı modellerin otomatik STEP dönüşümü yapılmakta ve daha sonra STEP dosyası değerlendirilerek, bu dosya yüz-kenar ilişki matrisine dönüştürülmektedir. Yeni tasarımın matrisine uyan veritabanından bir matris elde edilerek bu iki matris arasında boyutsal olmayan bir benzerlik değerlendirmesi yapılmaktadır. İki matriste temsil edilen yüzey komşuluk ilişkileri ve boyutsal olmayan nitelikler arasında tam bir eşleme var ise, parçalar arasında ölçekleme değerlendirmesi yapılmaktadır. Ölçekleme değerlendirmesinde, iki matrisin boyutsal nitelikleri arasında bir oran aranmaktadır. Bu oran var ise yaklaşım iki parça arasında ölçekleme oranı hesaplamaktadır.Öğe Bilgisayar destekli tasarımda parametrik dişli çark uygulamaları(2010) Ayyıldız, Mustafa; Çiçek, Adem; Kara, FuatBu çalışmada, dişli çark ve dişli çark çiftlerinin BDT (Bilgisayar Destekli Tasarım) ortamında parametrik olarak çizimi ve modellenmesi için bir yazılım geliştirilmiştir. Yazılım geliştirmede, yaygın bir kullanım alanına sahip ve diğer programlama dillerine göre oldukça basit olan Visual BASIC ve AutoLISP programlama dillerinin etkileşimli olarak kullanıldığı karma bir programlama yapısı tercih edilmiştir. Sistemde, modül, diş sayısı, iletim oranı vb. gibi parametreler kullanıcı tarafından girilerek, dişli çark veya dişli çark çiftleri boyutlandırılmakta ve daha sonra BDT ortamında dişli çarkların çizimi veya modellenmesi otomatik olarak yapılabilmektedir. Bu çalışma, dişli çark çizimi ve modellenmesi için tasarımcıya hızlı ve işlevsel bir yardımcı program alternatifi sunmaktadır.Öğe CNC tel erozyon için tasarlanan DXF tabanlı bir BDT/BDİ sistemi(2008) Çiçek, AdemBu makalede, CNC tel erozyon tezgahı için geliştirilen DXF tabanlı bir BDT/BDİ sistemi sunulmuştur. BDT/BDİ sistemi, BDT ortamında tasarlanan 2 boyutlu çizimler için hem bir otomatik CNC kod türetme modülünü, hem de takım yolunu canlandıran bir simülasyon modülünü içermektedir. Bilgisayarda yazılım geliştirmek için Visual BASIC ve Visual LISP programlama dilleri kullanılmıştır. Geliştirilen programla, BDT ortamında tasarlanan 2 boyutlu çizimler otomatik olarak DXF formatına dönüştürülmüş ve bu formatta temsil edilen geometrik unsurlar değerlendirilerek çizime ait bilgiler çıkarılmıştır. Bu bilgiler kenar eğrisi tipi (doğru, çember, yay, vs.), kenar eğrilerinin koordinatları (başlangıç ve bitiş noktaları, merkez noktaları, vs.) ve bazı kenar eğrisi nitelikleridir (yarıçap, yayın başlangıç ve bitiş açıları, vs.). Bu bilgiler kullanılarak BDT ortamında kullanıcı tarafından tasarlanan herhangi bir çizim için takım yolu oluşturulabilmekte ve FANUC formatına uygun CNC kodları türetilebilmektedir. Aynı zamanda, takım yoluna uygun olarak takım hareketi BDT ortamında canlandırılabilmektedir.Öğe Dişlilerin uzman sistem tabanlı tanımlanması ve detaylı boyutlarının çıkarılması(2008) Çiçek, AdemBu makalede, bir BDT sisteminde tasarlanan katı modellerden düz ve helis dişlileri tanımlamak ve detaylı boyutlarını çıkarmak için bir uzman sistem yazılımı geliştirilmiş ve dişli tanımlama sistemine entegre edilmiştir. Dişlilere ait BDT (Bilgisayar Destekli Tasarım) modellerinin STEP (Standard for the exchange of product data model) dosyaları tüm sisteme girdi olarak kullanılmıştır. Algoritma iki aşamada yürütülmektedir. İlk aşamada, uzman sistem için tasarlanan bir bilgi tabanında temsil edilen kurallar ve herhangi bir dişliye ait BDT modelinin STEP fiziksel dosyası muhakeme edilerek düz ve helis dişliler uzman sistem tarafından tanımlanmaktadır. İkinci aşamada, tanımlanan dişlilerin detaylı boyutları, STEP dosyasından elde edilen veri sayesinde çıkarılmaktadır. Geliştirilen yaklaşım, bu çalışmada sadece düz ve helis dişlilere uygulanmasına rağmen, farklı BDT/BDİ (Bilgisayar Destekli İmalat) uygulamaları için cıvata, somun, rondelâ, boru, vs. gibi farklı parça ailelerine başarı ile uygulanabilir.Öğe Effect of cutting conditions on wear performance of cryogenically treated tungsten carbide inserts in dry turning of stainless steel(Elsevier Sci Ltd, 2016) Özbek, Nurse Altan; Çiçek, Adem; Gülesin, Mahmut; Özbek, OnurIn this study, the effects of cryogenic treatment on tool wear of uncoated tungsten carbide inserts were investigated in the turning of AISI 316 stainless steel. It was found that notch wear appeared at low and medium cutting speeds, while flank wear and crater wear formed at all combinations of the process parameters selected for turning. In addition, treated inserts exhibited superior wear performance to untreated ones. This can be attributed to high wear resistance and low thermal conductivity of treated inserts. The results were verified by analyses of microstructure and hardness, image processing and Xray diffraction. (C) 2015 Elsevier Ltd. All rights reserved.Öğe Effect of Deep Cryogenic Treatment on Wear Resistance of AISI 52100 Bearing Steel(Springer India, 2014) Güneş, İbrahim; Çiçek, Adem; Aslantaş, Kubilay; Kara, FuatIn this study, the effects of deep cryogenic treatment (DCT) on the wear resistance of AISI 52100 bearing steel were investigated. For this purpose, a number of bearing steel samples were held for different times (12, 24, 36, 48, 60 h) at deep cryogenic temperatures (-145 degrees C). The wear experiments were carried out in a ball-disk arrangement, by applying loads of 10 and 20 N and a sliding velocity of 0.15 m/s. After conducting the experimental studies, 36 h was found to be the optimal holding time. At this holding time, the wear rate and friction coefficient were decreased, while the hardness reached to maximum values. It was observed that DCT led to significant microstructural changes, which resulted in improved tribological properties.Öğe Effects of Deep Cryogenic Treatment on the Wear Resistance and Mechanical Properties of AISI H13 Hot-Work Tool Steel(Springer, 2015) Çiçek, Adem; Kara, Fuat; Kıvak, Turgay; Ekici, Ergün; Uygur, İlyasIn this study, a number of wear and tensile tests were performed to elucidate the effects of deep cryogenic treatment on the wear behavior and mechanical properties (hardness and tensile strength) of AISI H13 tool steel. In accordance with this purpose, three different heat treatments (conventional heat treatment (CHT), deep cryogenic treatment (DCT), and deep cryogenic treatment and tempering (DCTT)) were applied to tool steel samples. DCT and DCTT samples were held in nitrogen gas at -145 degrees C for 24 h. Wear tests were conducted on a dry pin-on-disk device using two loads of 60 and 80 N, two sliding velocities of 0.8 and 1 m/s, and a wear distance of 1000 m. All test results showed that DCT improved the adhesive wear resistance and mechanical properties of AISI H13 steel. The formation of small-sized and uniformly distributed carbide particles and the transformation of retained austenite to martensite played an important role in the improvements in the wear resistance and mechanical properties. After cleavage fracture, the surfaces of all samples were characterized by the cracking of primary carbides, while the DCT and DCTT samples displayed microvoid formation by decohesion of the fine carbides precipitated during the cryo-tempering process.Öğe Evaluation of machinability of hardened and cryo-treated AISI H13 hot work tool steel with ceramic inserts(Elsevier Sci Ltd, 2013) Çiçek, Adem; Kara, Fuat; Kıvak, Turgay; Ekici, ErgünThe positive effects of deep cryogenic treatment on the wear resistance of cutting tools and workpiece material are well known; however, no information has been reported about the effect on the machinability of cryo-treated tool steel in hard turning. In order to investigate the effects of cryogenic treatment on the machinability of hardened and cryo-treated tool steel, a number of investigations were performed on the hard turning of cryo-treated AISI H13 hot-work tool steel with two ceramic inserts under both dry and wet cutting conditions. Three categories of the hot-work tool steel were turned in the machinability studies: conventional heat treated (CHT), cryo-treated (CT) and cryo-treated and tempered (m). Experimental results showed that the lowest wear and surface roughness (Ra) values were obtained in the turning of the CTT samples. Additionally, in terms of main cutting force (Fc), surface roughness (Ra) and tool wear, Ti[C, N]-mixed alumina inserts (CC650) showed a better performance than SiC whisker-reinforced alumina inserts (CC670) under both dry and wet cutting conditions. The use of cutting fluid slightly improved the machinability of the tool steel. (C) 2013 Elsevier Ltd. All rights reserved.Öğe EXPERT SYSTEM BASED IDENTIFICATION AND EXTRACTION OF DETAILED DIMENSIONS FOR GEARS(Gazi Univ, Fac Engineering Architecture, 2008) Çiçek, AdemIn this paper, an expert system software has been developed and integrated to identify the spur and helical gears, and to extract their detailed dimensions from solid models designed in a CAD system. The STEP files of CAD models of gears have been used as input to the whole system. The algorithm is executed in two main stages. In the first stage, the spur and helical gears are identified by expert system by reasoning STEP physical file of CAD model belonging to any gear and rules represented in a knowledge base constructed for the expert system. In the second stage, detailed dimensions of the identified gears are extracted via data obtained from STEP file. Although the developed approach is only applied to spur and helical gears in this study, it can be successfully implemented to different part families such as bolts, nuts, washers, pipes etc. for other CAD/CAM applications.Öğe FATIGUE LIFE PREDICTIONS OF METAL MATRIX COMPOSITES USING ARTIFICIAL NEURAL NETWORKS(Polska Akad Nauk, Polish Acad Sciences, Inst Metall & Mater Sci Pas, 2014) Uygur, İlyas; Çiçek, Adem; Toklu, Ethem; Kara, Resul; Sarıdemir, SuatIn this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.Öğe Investigation of the effects of cryogenic treatment applied at different holding times to cemented carbide inserts on tool wear(Elsevier Sci Ltd, 2014) Özbek, Nurse Altan; Çiçek, Adem; Gülesin, Mahmut; Özbek, OnurCutting tool costs is one of the most important components of machining costs. For this reason, tool life should be improved using some methods such as cutting fluid, optimal cutting parameters, hard coatings and heat treatment. Recently, another one of the methods commonly used to improve tool life is cryogenic treatment. This study was designed to evaluate the effects of different holding times of deep cryogenic treatment on tool wear in turning of AISI 316 austenitic stainless steel. The cemented carbide inserts were cryogenically treated at -145 degrees C for 12, 24, 36, 48 and 60 h. Wear tests were conducted at four cutting speeds (100, 120, 140 and 160 m/min), a feed rate of 0.3 mm/rev and a 2.4 mm depth of cut under dry cutting conditions. The wear test results showed that flank wear and crater wear were present in all combinations of the cutting parameters. However, notch wear appeared only at lower cutting speeds (100 and 120 m/min). In general, the best wear resistance was obtained with cutting inserts cryogenically treated for 24 h. This case was attributed to the increased hardness and improved microstructure of cemented carbide inserts. These improvements were confirmed through hardness, image processing, and XRD analyses. (C) 2014 Elsevier Ltd. All rights reserved.Öğe Machinability of AISI 316 Austenitic Stainless Steel With Cryogenically Treated M35 High-Speed Steel Twist Drills(Asme, 2012) Çiçek, Adem; Uygur, İlyas; Kıvak, Turgay; Özbek, Nursel AltanIn this paper, machinability of AISI 316 austenitic stainless steel was investigated using cryogenically treated and untreated high-speed steel (HSS) twist drills. Machinability of AISI 316 austenitic stainless steel was evaluated in terms of thrust force, tool life, surface roughness, and hole quality of the drilled holes. Experimental results showed from 14% to 218% improvements for treated tool lives. Thrust force, surface roughness, and hole quality are better with treated drills when compared with untreated drills. These improvements were mainly attributed to formation of fine and homogeneous carbide particles and transformation of retained austenite to martensite. Microhardness and microstructure observations verified these formations. [DOI: 10.1115/1.4007620]Öğe Modelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis(Assoc Mechanical Engineers Technicians Slovenia, 2012) Çiçek, Adem; Kıvak, Turgay; Samtaş, Gürcan; Çay, YusufIn this study, the effects of cutting parameters (i.e., cutting speed, feed rate) and deep cryogenic treatment on thrust force (Ff) have been investigated in the drilling of AISI 316 stainless steel. To observe the effects of deep cryogenic treatment on thrust forces, M35 HSS twist drills were cryogenically treated at -196 degrees C for 24 h and tempered at 200 degrees C for 2 h after conventional heat treatment. The experimental results showed that the lowest thrust forces were measured with the cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. The scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. The ANN results showed that the SCG learning algorithm with five neurons in the hidden layer produced the coefficient of determinations (R-2) of 0.999907 and 0.999871 for the training and testing data, respectively. In addition, the root mean square error (RMSE) was 0.00769 and 0.009066, and the mean error percentage (MEP) was 0.725947 and 0.930127 for the training and testing data, respectively.
