1,721,051 research outputs found
PARAMETRIC GEAR WHEEL APPLICATIONS IN COMPUTER AIDED DESIGN
Full text linkKARA, Fuat/0000-0002-3811-3081WOS: 000285689700024In this study, a software has been developed to parametrically draw and model the gear wheels and gear wheel pairs in a CAD (Computer Aided Design) environment. In the software development, a hybrid programming structure using Visual BASIC and AutoLISP programming languages is used for the proposed software. The programming languages are easy and commonly used. In the software system, gear wheels or gear wheel pairs are sized by inputting parameters such as modules, transmission ratios, teeth numbers, etc. and then they are automatically drawn and modeled in CAD environment. This study presents an auxiliary program which is rapid and functional to the designer for drawing and modeling of gear wheels
Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network
Full text linkAslantas, Kubilay/0000-0003-4558-4516; KARA, Fuat/0000-0002-3811-3081WOS: 000366805900005In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al2O3 + TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R-2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures. (C) 2015 Elsevier B.V. All rights reserved
MULTIPLE REGRESSION AND ANN MODELS FOR SURFACE QUALIFICATION OF CRYOGENICALLY-TREATED AISI 52100 BEARING STEEL
Full text linkKARA, Fuat/0000-0002-3811-3081WOS: 000330146300008This paper focuses on 2 different models, the multiple regression method and the artificial neural network (ANN), for predicting surface roughness (R-a). Experiments were conducted to measure surface roughness in the cylindrical grinding of AISI 52100 bearing steel which had been conventionally heat-treated and deep cryogenically treated (-145 degrees C). In order to compare the effects of holding time at the deep cryogenic temperatures, 5 different holding times (12, 24, 36, 48 and 60 h) were employed to obtain the optimum R-a. The cylindrical grinding test results showed that optimum R-a values were obtained on specimens cryogenically treated for 36 h. In addition, the prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability. Moreover, due to a higher determination coefficient (R-2) and lower root-mean-square error (RMSE) and mean error percentage (MEP), the ANN was notably successful in predicting the R-a.Karabuk University Scientific Research Project DivisionKarabuk University [KBU-BAP-11/2-DR-003]The authors wish to place their sincere thanks to Karabuk University Scientific Research Project Division for financial support for the Project No KBU-BAP-11/2-DR-003
Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network
Full text linkKARA, Fuat/0000-0002-3811-3081WOS: 000316432700019This study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (air-fuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (Levenberg-Marquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines. (C) 2012 Elsevier Ltd. All rights reserved
Evaluation of machinability of hardened and cryo-treated AISI H13 hot work tool steel with ceramic inserts
Full text linkKARA, Fuat/0000-0002-3811-3081WOS: 000325840700068The 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.Mace University Scientific Research Project Division [BAP 2011.03.02.065]The authors wish to place their sincere thanks to Mace University Scientific Research Project Division for financial support for the Project No: BAP 2011.03.02.065
Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network
Full text linkKARA, Fuat/0000-0002-3811-3081; ERKAN, Omer/0000-0002-9428-4299WOS: 000322706000012Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R-2 and lower RMSE and MEP
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Investigation of the effect of shallow and deep cryogenic process on wear and impact performance of CPOH tool steel
Full text linkCPOH soğuk iş takım çeliği; plastik ve sac metal kalıpları, ovalama makaraları ve yonga bıçakları gibi yüksek aşınma ve darbe dayanımı gerektiren yerlerde kullanılmaktadır. Endüstride geniş bir kullanım alanına sahip olan CPOH soğuk iş takım çeliği hakkında, literatürde sınırlı sayıda araştırma yapıldığı görülmektedir. Bu çalışma ile derin ve sığ kriyojenik işlem uygulanan CPOH soğuk iş takım çeliğinin mikroyapısı ve mekanik özelliklerinde meydana gelen değişimler araştırılmıştır. Deney numuneleri sadece geleneksel ısıl işlem uygulanan ''CHT'' 18 saat bekleme süresinde ve -80 °C sıcaklıkta sığ kriyojenik işlem uygulanan ''SCT-18'' 18 ve 36 saat bekleme sürelerinde -196 °C sıcaklıkta derin kriyojenik işlem uygulanan ''DCT-18'' ve ''DCT-36'' olmak üzere 4 farklı grupta sınıflandırılmıştır. Kriyojenik işlem uygulanan numunelere 200 °C sıcaklıkta 2 saat boyunca temperleme işlemi yapılmıştır. Numunelerin mikro sertlik, makro sertlik, aşınma dayanımı darbe enerjisi ve mikro yapıları incelenmiştir. CPOH takım çeliğine uygulanan kriyojenik işlem neticesinde makro sertlik değerinde %1,1 ve mikro sertlik değerinde ise %2,17 olmak üzere en fazla oransal artış SCT-18 deney numunesinde ölçülmüştür. Kriyojenik işlem uygulanan SCT-18, DCT-18 ve DCT-36 isimli deney numunelerinin sürtünme katsayısı değerlerinde sırasıyla %13,47, %11,77 ve %3,71 oranında iyileşmeler meydana gelmiştir.CPOH cold work tool steel; It is used in places where high wear and impact resistance is required such as plastic and sheet metal molds, scouring rollers and chip blades. It is seen that limited research has been done in the literature about CPOH cold work tool steel, which has a wide usage area in the industry. In this study, the changes in the microstructure and mechanical properties of CPOH cold work tool steel, which were subjected to deep and shallow cryogenic treatment, were investigated. The test specimens were only tested with conventional heat treated "CHT" 18 hours holding time and -80 °C shallow cryogenic treatment "SCT-18" 18 and 36 hours deep cryogenic treatment at -196 °C temperature. It is classified in 4 different groups as DCT-18'' and ''DCT-36''. The cryogenically treated samples were tempered at 200 °C for 2 hours. Microhardness, macro hardness, abrasion resistance, impact energy and microstructure of the samples were investigated. As a result of the cryogenic treatment applied to CPOH tool steel, the highest proportional increase in macro hardness value of 1.1% and microhardness value of 2.17% was measured in the SCT-18 test sample. The friction coefficient values of the experimental samples named SCT-18, DCT-18 and DCT-36, which were subjected to cryogenic treatment, were improved by 13.47%, 11.77% and 3.71%, respectively
Optimization by the taguchi method of effect on the surface roughness of cryogenic treatment applied to cutting tools
Full text linkYÖK Tez No: 509317Bu çalışmada, AISI O2 (DIN 1.2842) soğuk iş takım çeliğinin işlenmesinde kesici takımlara uygulanan derin kriyojenik işlemin (DCT) yüzey pürüzlülüğü (Ra), kesme kuvveti (Fc), takım aşınması ve mikrosertlik üzerine etkileri araştırılmıştır. Bununla birlikte, optimum Ra ve Fc sonucunu veren kesme parametreleri Taguchi optimizasyon metodu ile belirlenmiştir. Tornalama deneyleri, Taguchi L16 (43) ortogonal (dikey) dizinine göre yapılmış, deney sonuçlarının değerlendirilmesinde sinyal/gürültü (S/G) oranı esas alınmıştır. Deneylerde kaplamalı (MT-TiCN+Al2O3+TiN) ve kaplamasız karbür (WC+Co+TiC+TaC) takımlar kullanılmıştır. Rieltveld analizi ile takım mikroyapısındaki karbürlerin oransal değişimi tespit edilmiştir. Son olarak mikrosertlik ölçümleri ile kriyojenik işlemin sertlik üzerindeki etkisi ortaya koyulmuştur. Taguchi analizi sonucu, yüzey pürüzlülüğü için optimum sonuçlar kriyojenik işlemli kaplamasız takım ile 250 m/dak kesme hızında ve 0,08 mm/dev ilerleme hızında elde edilmiştir. ANOVA sonuçlarına göre, yüzey pürüzlülüğü üzerindeki en etkili parametrenin ilerleme hızı (% 80,20), daha sonra sırasıyla kesici takım tipi (% 12,98) ve kesme hızı (% 5,86) olduğu görülmüştür. Kesme kuvveti için en düşük değerleri veren parametreler sırasıyla kesici takım türü, kesme hızı ve ilerleme hızı için kriyojenik işlemli kaplamalı takım, 250 m/dak ve 0,08 mm/dev olarak bulunmuştur. Fc değerleri için gerçekleştirilen ANOVA sonuçlarına göre kesme kuvveti üzerinde en etkili parametrenin % 85,70 oranıyla ilerleme hızı olduğu görülmüştür. Daha sonra kesici takım türü % 7,94 ve kesme hızı % 4,60 ile en az etkiye sahip parametreler olarak sıralanmıştır. Takım ömrü açısından kriyojenik işlem hem kaplamasız hem de kaplamalı karbür kesici takımlarda olumlu sonuçlar sergilemiştir. Bu bağlamda kaplamasız ve kaplamalı karbür kesici takımlarda sırasıyla % 15 ve % 11 oranlarında iyileşme sağlanmıştır. Rieltveld analizi sonucu, kriyojenik işlemin takımlarda karbür yüzdelerini arttırdığı belirlenmiştir. Mikrosertlik ölçümleri sonucunda, kriyojenik işlem sonrasında kaplamasız ve kaplamalı karbür takımların sertlikleri sırasıyla % 4,6 ve % 5,15 oranlarında arttığı görülmüştür.In this study, the effects on the surface roughness (Ra), cutting force (Fc), tool wear and microhardness of the deep cryogenic treatment (DCT) applied to carbide cutting tools in the machining of the AISI O2 (DIN 1.2842) cold work tool steel were investigated. However, the cutting parameters giving the optimum Ra and Fc are determined by the Taguchi optimization method (L16-43). Coated (MT-TiCN+Al2O3+TiN) and uncoated (WC+Co+TiC+TaC) carbide tools were used in the experiments. Finally, microhardness measurements have shown the effect of cryogenic process on hardness. The optimum results for surface roughness were obtained with a cryogenically-treated uncoated carbide cutting tool at a cutting speed of 250 m/min and a feed rate of 0.08 mm/rev. According to ANOVA results, the most effective parameter on the surface roughness was found to be the feed rate (80.20 %), followed by the cutting tool type (12.98 %) and the cutting speed (5.86 %). The parameters giving the lowest cutting forces were found to be cryogenically treated coated carbide cutting tool, 250 m/min and 0.08 mm/rev for cutting tool type, cutting speed and feed rate, respectively. According to the ANOVA results for Fc values, the most effective parameter on cutting force was found to be the feed rate of 85.70 %. Then, the cutting tool type is listed as 7.94 % and the cutting speed with 4.60 % as the least effective parameters. In terms of tool life, the cryogenic process has shown positive results both in uncoated and coated carbide cutting tools. In this context, 15 % and 11 % improvement was achieved in uncoated and coated-carbide cutting tools, respectively. According to the results of the Rieltveld analysis, it was determined that the carbide percentages increased in both tool groups after the deep cryogenic treatment. As a result of microhardness measurements, the hardness of uncoated and coated carbide sets after cryogenic treatment increased by 4.6 % and 5.15 %, respectively
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