Browsing by Author "Yuce, Celalettin"
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Publication A comparative 3d finite element computational study of stress distribution and stress transfer in small-diameter conical dental implants(Univ Osijek, Tech Fac, 2021-12-01) Doğan, Oğuz; Dhanasekaran, Lokesh; Khandaker, Morshed; Kalay, Onur Can; Karaman, Hasan; Karpat, Fatih; KARPAT, FATİH; Doğan, Oguz; DOĞAN, OĞUZ; Yuce, Celalettin; YÜCE, CELALETTİN; Karpat, Esin; KARPAT, ESİN; Dhanasekaran, Lokesh; Khandaker, Morshed; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik Elektronik Mühendisliği.; 0000-0001-8643-6910; 0000-0001-8474-7328; 0000-0003-1387-907X; 0000-0001-5985-7402; A-5259-2018; GXH-1702-2022; AAV-7897-2020; R-3733-2017The implant design is one of the main factors in implant stability because it affects the contact area between the bone and the implant surface and the stressstrain distribution at the bone-implant interface. In this study, the effect of different groove geometries on stress-strain distributions in small-diameter conical implants is investigated using the finite element method (FEM). Four different thread models (rectangular, buttressed, reverse buttressed, and symmetrical profile) are created by changing the groove geometry on the one-piece implants, and the obtained results are compared. The stress shielding effect is investigated through the dimensionless numbers that characterize the load-sharing between the bone-implant. It is determined that the lowest stress distribution is observed with rectangular profiled groove geometry. Besides, it is obtained that the buttressed groove geometry minimizes the stress effects transmitted to the periphery of the implant. The symmetrical profiles had better performance than rectangular profiles in stress transfer.Publication Fault diagnosis with deep learning for standard and asymmetric involute spur gears(Amer Soc Mechanical Engineers, 2021-01-01) Yuce, Celalettin; Dogan, Oguz; Karpat, Fatih; Dirik, Ahmet Emir; KARPAT, FATİH; DİRİK, AHMET EMİR; Kalay, Onur Can; Korcuklu, Burak; KORCUKLU, BURAK; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi; 0000-0001-8474-7328; 0000-0002-6200-1717; 0000-0001-8643-6910; 0000-0003-1387-907X; A-5259-2018; R-3733-2017Gears are critical power transmission elements used in various industries. However, varying working speeds and sudden load changes may cause root cracks, pitting, or missing tooth failures. The asymmetric tooth profile offers higher load-carrying capacity, long life, and the ability to lessen vibration than the standard (symmefric) profile spur gears. Gearbox faults that cannot be detected early may lead the entire system to stop or serious damage to the machine. In this regard, Deep Learning (DL) algorithms have started to be utilized for gear early fault diagnosis. This study aims to determine the root crack for both symmefric and asymmefric involute spur gears with a DL-based approach. To this end, single tooth stiffness of the gears was obtained with ANSYS software for healthy and cracked gears (50-100%), and then the time-varying mesh stiffness (TVMS) was calculated. A six-degrees-of-freedom dynamic model was developed by deriving the equations of motion of a single-stage spur gear mechanism. The vibration responses were collected for the healthy state, 50% and 100% crack degrees for both symmefric and asymmefric tooth profiles. Furthermore, the white Gaussian noise was added to the vibration data to complicate the early crack diagnosis task. The main contribution of this paper is that it adapts the DL-based approaches used for early fault diagnosis in standard profile involute spur gears to the asymmefric tooth concept for the first time. The proposed method can eliminate the need for large amounts of training data from costly physical experiments. Therefore, maintenance strategies can be improved by early crack detection.