Austempered Ductile Iron (ADI) is widely implemented to achieve lightweight and optimized components in power transmissions (e.g., internal gear in planetary stages), by substituting steel castings or a combination of Ductile Iron and steel-assembled structures. In this paper, the contact fatigue of two ADI grades (ADI J/S900-8; ADI J/S1200-3) is investigated by disk-on-disk tests. The disk-on-disk contact is an analogy model of the tooth contact and represents the tribological conditions at a specific point on the path of contact. The experimental results cover the endurance limit as well as the short time fatigue behavior for both ADI variants. In addition, the pitting characteristics and microstructural changes are analyzed in order to get a further understanding of the fatigue behavior of ADI.

Today, designing the microgeometry of gears is often based on experience. Usually, objective values (Hertzian pressure, …) from a tooth contact analysis are evaluated. In many cases the manufacturing tolerances are not fully considered in gear design. This results in suboptimal operation as objective values can vary significantly.The objective of this study is to introduce a simulation based method for function-oriented tolerancing of the tooth flank microgeometry. Therefore, the tolerance field is considered by a weighted average grade for each microgeometry variation in the solution space. Based on this grade, the optimal solution regarding quality and stability can be achieved

Simulation models of the tooth contact can provide valuable information of the gearbox operational behavior for deviated gears. It is therefore possible to gather geometry and deviation information of the two gears in contact and then estimate how they will perform in the assembled unit before even mounting them. This procedure could save time and costs used to disassemble gear boxes which fail the end of line tests because of acoustic reasons. The major challenge using sophisticated simulation models, which can describe the tooth contact is the tremendous amount of calculation time they need to produce suit-able results. In a productive gearbox manufacturing process this time is just not available. Therefore, a method to predict the operational behavior faster, than with the current used simulation models is needed. Firstly the size of the problem is downsized by introducing the sum deviation surface. It allows to reduce the number of necessary input parameters for a gear topography description to eighteen factors. With the help of that sum deviation surface, 3000 variants within a given variation space are calculated. The resulting training dataset is used to develop a deep neural network meta-model of the gear contact, which can predict the characteristics of the transmission error under load. With the help of that meta-model, the excitation of a gear pair can be predicted faster than real-time.

In future aero engines, a planetary gearbox is to be integrated between fan and turbine to increase the efficiency and bypass ratio. This gearbox has to be monitored during operation to detect possible gearbox faults such as gear wear or gear pitting at an early stage. This paper presents a method consisting of vibration measurement, sensor-dependent feature extraction and support-vector machine (SVM)-based classification of pitting for gear condition monitoring. Several gears were loaded with a constant torque on a standardized back-to-back test rig to provoke pitting, and the pitting amount was captured during the tests with a camera. Features are extracted from accelerometers and an acoustic emission sensor, and based on the results of the visually recorded pitting surface, SVM classification is applied to identify the pitting defect. In this contribution, two different SVM classification approaches are investigated. One approach uses a Two-Class SVM, where tests from one gearset are used for SVM training and another approach utilizes a One-Class SVM based on outlier detection. Both methods show that single tooth pitting defects with a relative pitting area of less than 1 % can be effectively identified, whereas the One-Class SVM method showed a higher pitting detection accuracy.