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Murat  Tolga Ozkan
Murat Tolga Ozkan
Professor, Gazi University,Faculty of Technology, Industrial Design Engineering
Verified email at gazi.edu.tr - Homepage
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Cited by
Cited by
Year
Uzman Sistem Yaklaşımı ile Civata ve Disli Çark Seçimi
M ÖZKAN, M Gülesin
Turkish Journal of Engineering and Environmental Sciences 25 (3), 2001
36*2001
Experimental and artificial neural network study of heat formation values of drilling and boring operations on Al 7075 T6 workpiece
M ÖZKAN
Indian Journal of Engineering and Materials Sciences 20 (4), 2013
352013
The acute effects of preoperative ozone theraphy on surgical wound healing
H Sahin, T Simsek, H Turkon, Y Kalkan, F Ozkul, M Ozkan, M Erbas, ...
Acta cirurgica brasileira 31, 472-478, 2016
282016
Vibration prediction in drilling processes with HSS and carbide drill bit by means of artificial neural networks
YM Hasan Basri Ulas, Murat Tolga OZKAN
Neural computing and applications 31 (9), 5547-5562, 2019
252019
EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK MODEL FOR TURNING THE 50CrV4 (SAE 6150) ALLOY USING COATED CARBIDE/CERMET CUTTING TOOLS
MB Murat Tolga Ozkan, Hasan Basri Ulas
Materiali in technologie 48 (2), 227-237, 2014
222014
Experimental research and ANN modeling on the impact of the ball burnishing process on the mechanical properties of 5083 Al-Mg material
IT Hüdayim Basak, Murat Tolga Ozkan
Kovové materiály - Metallic Materials 57 (1), 61-74, 2019
192019
Determination of the stress concentration factor (Kt) in a rectangular plate with a hole under tensile stress using different methods
MT Ozkan, I Toktas
Materials testing 58 (10), 839-847, 2016
192016
Surface roughness during the turning process of a 50CrV4 (SAE 6150) steel and ANN based modeling
MT Ozkan
Materials Testing 57 (10), 889-896, 2015
172015
Turning processes investigation of materials austenitic, martensitic and duplex stainless steels and prediction of cutting forces using artificial neural network (ANN) techniques
HB Ulas, MT Ozkan
Indian Journal of Engineering and Materials Sciences (IJEMS) 26 (2), 93-104, 2019
142019
DETERMINATION OF THE NOTCH FACTOR FOR SHAFTS UNDER TORSIONAL STRESS WITH ARTIFICIAL NEURAL NETWORKS
İS Murat Tolga Ozkan, Cengiz Eldem
Materiali in technologie 48 (1), 81-91, 2014
14*2014
Determination of theoretical stress concentration factor for circular/ elliptical holes with reinforcement using analytical, finite element method and artificial neural network …
FE Murat Tolga OZKAN
Neural Computing &Applications, 2021
132021
Determination of stress concentration factors for shafts under tension
MT Ozkan, F Erdemir
Materials Testing 62 (4), 413-421, 2020
132020
Notch sensitivity factor calculationin the design of shafts using artificial neural network system
MT OZKAN
Energy Education Science and Technology Part A: Energy Science and Research …, 2012
122012
Determination of stress concentration factor (Kt) for a crankshaft under bending loading: an artificial neural networks approach
İ Toktaş, MT ÖZKAN, F Erdemir, N Yuksel
Politeknik Dergisi 23 (3), 813-819, 2020
82020
Performance of coated and uncoated carbide/cermet cutting tools during turning
HB Ulas, M Bilgin, HK Sezer, MT Ozkan
Materials Testing 60 (9), 893-901, 2018
82018
Notch sensitivity factor determination with artificial neural network for shafts under the bending stress
MT Özkan, C Eldem, E Köksal
82013
Estimations of stress concentration factors Cw/Kts for helical circular/square cross sectional tension-compression springs and artificial neural network modelling
MT Ozkan, I Toktas, SK Doganay
Journal of Polytechnic 23 (3), 901-908, 2020
62020
Estimations of stress concentration factors (Cw/Kts) for helical circular/square cross sectional tension-compression springs and artificial neural network modelling
MT ÖZKAN, İ TOKTAŞ, K DOĞANAY
Politeknik Dergisi 23 (3), 901-908, 2020
62020
Bilgisayar destekli helisel yay tasarımı ve sonlu elemanlar analizi
M ÖZKAN, K DÜNDAR, F GÜMÜŞ
TÜBAV Bilim Dergisi 2 (2), 199-210, 2009
42009
Plastik parçalarda bir esneyerek kilitlenen bağlantı modelinin modal analizi
F Erdemir, MT Ozkan
Politeknik Dergisi 22 (4), 927-933, 2019
32019
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