Simulation of induced axial forces on planet gear bearings at example of ZF’s 8-speed automatic transmission

2021 ◽  
pp. 469-486
Author(s):  
S. Dussinger ◽  
T. Wiedemann ◽  
B. Harter ◽  
B. Wiedenmann
Author(s):  
Goutam Chatterjee ◽  
Lung-Wen Tsai

Abstract The enumeration of epicyclic gear mechanisms in the form of graphs gives rise to the need of a methodology for reverse transformation, that is, for constructing the mechanisms from graphs. This paper addresses the issue by discretizing an epicyclic gear mechanism into Fundamental Geared Entities. Further, these geared entities are shown to be a conglomeration of four primitives; namely, the carrier, sun, ring, and the planet gear. An algorithm is formulated to create the entities from a graph by using these primitives. The entities are then connected together to form a mechanism.


1996 ◽  
Vol 118 (3) ◽  
pp. 405-411 ◽  
Author(s):  
G. Chatterjee ◽  
Lung-Wen Tsai

The enumeration of epicyclic gear mechanisms in the form of graphs gives rise to the need of a methodology for reverse transformation, that is, for constructing the mechanisms from graphs. This paper addresses the issue by discretizing an epicyclic gear mechanism into Fundamental Geared Entities. Further, these geared entities are shown to be a conglomeration of four primitives; namely, the carrier, sun, ring, and the planet gear. An algorithm is formulated to create the entities from a graph by using these primitives. The entities are then connected together to form a mechanism.


2021 ◽  
Vol 64 (5) ◽  
pp. 1483-1498
Author(s):  
Hyun-Woo Han ◽  
Jung-Su Han ◽  
Woo-Jin Chung ◽  
Ji-Tae Kim ◽  
Young-Jun Park

HighlightsPrediction of synchronization time was performed for a power-shift transmission.We derived an analytical equation for synchronization time and developed a multi-body dynamics model.Model results were compared with results of a power-shift test on a synchronizer.Reduced computation and design time was achieved for automatic transmission design.Abstract. Synchronization time determines the capacity of a shift actuator for an automatic transmission system. Existing approaches for measuring this time only consider one rotational inertia and therefore cannot be applied to the power-shift transmission (PST) of a tractor with wet multi-plate clutches on both sides of the synchronizer. This study aims to predict the PST synchronization time by considering time-varying axial forces as first-order functions and the equivalent rotational inertias of the hub and the gear. First, we derive an analytical equation for the synchronization time. We then develop a multi-body dynamics (MBD) model that includes the drag torque of the wet multi-plate clutches. The MBD model is composed of a synchronizer, a linkage, and an output shaft of a shift actuator as a rigid-body system. A power-shift test was performed on the synchronizer at two shift stages requiring the maximum shift force of the system. The torque of the shift actuator (the input of the shift system) and the angular displacement of the output shaft of the shift actuator (the output of the shift system) were measured. The results of the simulation model were then compared with those of the shift test. Compared with the test results, the simulation results were validated within 7.63% accuracy, based on the maximum value for the torque of the shift actuator. The proposed equation was validated within a maximum error range of 8.25%. The proposed equation did not consider drag torque of the wet multi-plate clutches because that torque is much smaller than the cone torque of the synchronizer in the target shift system. The proposed equation can reduce computation time and will enable more precise sizing of the synchronizer and shift actuator in the early design stages of automatic transmissions. Keywords: Multi-body dynamics, Power-shift transmission, Synchronization time, Synchronizer, Tractor transmission.


2019 ◽  
Vol 7 (SI-TeMIC18) ◽  
Author(s):  
Norhanifah Abdul Rahman ◽  
Matzaini Katon Katon ◽  
Nurina Alya Zulkifli Zulkifli

Automatic Transmission (AT) system is efficient in the aspects of vehicle safety, comfort, reliability and driving performance. The objectives of this paper are to collect the oil samples from AT systems of engine bus according to manufacturer's recommendations and analyse collected oil samples using oil analysis technique. The sample transmission fluid which was taken from the AT gearbox has been experimentally analyzed. The oil samples were taken with an interval of 5,000km, 30,000km, 50,000km, 80,000km, 180,000km and 300,000km for AT bus operation. These samples then have been analyzed by comparing between new and used transmission fluid using Fourier Transform Infrared (FTIR) spectroscopy. Oil analysis by FTIR is a form of Predictive Maintenance (PdM) to avoid major failure in machine elements. Most machine elements are not easily accessible in the transmission system. Having a reliable technique would avoid the needs to open the components unnecessarily, hence, help to prevent catastrophic failure which are very costly, and ease of regular monitoring. In order to identify the major failures of automatic gearbox, forecasts can be made regarding the lube transmission fluid analysis test. By using this test, the minor problems can be determined before they become major failures. At the end of this research, the wear particles profile for interval mileage of AT system was obtained. Keywords: Wear, Automatic Transmission (AT), Transmission fluid, Fourier Transform Infrared (FTIR), Oil analysis.


2011 ◽  
Vol 314-316 ◽  
pp. 1218-1221
Author(s):  
Hao Min Huang

Conventional methods of design to be completed ordinary hydraulic transmission gear gearbox design, but for such a non-planet-rule entity, and the deformation of the planet-gear contact stress will have a great impact on the planet gear, it will be very difficult According to conventional design. In this paper, ANSYS software to the situation finite element analysis, the planetary gear to simulate modeling study.


2021 ◽  
Vol 493 ◽  
pp. 115844
Author(s):  
He Dai ◽  
Xinhua Long ◽  
Feng Chen ◽  
Jie Bian

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