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    A comparative experimental study on the impact strength of standard and asymmetric involute spur gears
    (Elsevier Sci Ltd, 2021) Kalay, Onur Can; Dogan, Oguz; Yilmaz, Tufan Gurkan; Yüce, Celalettin; Karpat, Fatih
    Gears are one of the main components of the power transmission systems and are used in various fields. Problems caused by sudden load changes in mobile systems are frequently encountered today. Gear dynamics have become more influential due to demands of high power transmission capability, long life, and low-cost. However, inertial forces caused by accelerated movements of gear can have unpredictable results. The impact loads must be calculated correctly. It is inconvenient to determine the impact strength of gear via standard drop-weight test rig due to inhomogeneity and complex geometries. This study investigates how the tooth profile affects the impact load on the involute spur gears. For this reason, a special test setup and experimental approach was proposed to examine the influence of the asymmetric profile on the impact strength. It was observed that the peak force values increased by approximately 15.3% when using 20/30 degrees asymmetric profile gears in comparison with the 20 degrees/20 degrees standard design. This improvement can reach up to 25.8% in terms of peak force energy. The results indicate that the proposed novel test setup and the experimental method can be used for measuring the impact strength of asymmetric involute gears.
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    FAULT DIAGNOSIS WITH DEEP LEARNING FOR STANDARD AND ASYMMETRIC INVOLUTE SPUR GEARS
    (American Society of Mechanical Engineers (ASME), 2021) Karpat, Fatih; Dirik, Ahmet Emir; Kalay, Onur Can; Yüce, Celalettin; Doğan, Oğuz; Korcuklu, Burak
    Gears 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 (symmetric) 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 symmetric and asymmetric 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 symmetric and asymmetric 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 asymmetric 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.