5-axis double-flank CNC machining of spiral bevel gears via custom-shaped tools—Part II: physical validations and experiments

Recently, a new methodology for 5-axis flank computer numerically controlled (CNC) machining, called double-flank machining, has been introduced (see “5-axis double-flank CNC machining of spiral bevel gears via custom-shaped milling tools—Part I: Modeling and simulation”). Certain geometries, such as curved teeth of spiral bevel gear, admit this approach where the machining tool has tangential contact with the material block on two sides, yielding a more efficient variant of flank machining. To achieve high machining accuracy, the path-planning algorithm, however, does not look only for the path of the tool, but also for the shape of the tool itself. The proposed approach is validated by series of physical experiments using an abrasive custom-shaped tool specifically designed for a particular type of a spiral bevel gear. The potential of this new methodology is shown in the semifinishing stage of gear manufacturing, where it outperforms traditional ball end milling by an order of magnitude in terms of machining time, while keeping, or even improving, the machining error.

Modeling and analysis of transient lubrication characteristic of the helicopter main transmission spiral bevel gears in point contacts

An engineering calculation model is introduced for point-contact elastohydrodynamic lubrication analysis of spiral bevel gears. This model can analyze transient lubrication characteristics of spiral bevel gears. The influence of the angle between the lubricant entrainment and the minor axis of the contact ellipse is included in this model. The contact parameters of the spiral bevel gear are calculated, which will change with time during the meshing process. The variation of lubricant film thickness during the meshing process of spiral bevel gears is unraveled. Due to the influence of entrainment velocity, the oil film thickness at the out mesh side is smaller than that at the enter mesh side under the same contact force. It is evident that the higher the pressure is, the larger the contact area will be. Meanwhile, the thickness of the oil film is reduced, and the oil film distribution in the contact area is relatively uniform. Taking helicopter main transmission spiral bevel gears as an example, this study finally calculates the distribution characteristics of the oil film thickness of the spiral bevel gear, and solves the lubrication performance of the spiral bevel gear under different working conditions.

An Efficient Approach to the Five-Axis Flank Milling of Non-Ferrous Spiral Bevel Gears

Five-axis flank milling has been applied in industry as a relatively new method to cut spiral bevel gears (SBGs) for its flexibility, especially for the applications of small batches and repairs. However, it still has critical inferior aspects compared to the traditional manufacturing ways of SBGs: the efficiency is low, and the machining accuracy may not ensure the qualified meshing performances. To improve the efficiency, especially for cutting non-ferrous metals, this work proposes an approach to simultaneously cut the tooth surface and tooth bottom by a filleted cutter with only one pass. Meanwhile, the machining accuracy of the contact area is considered beforehand for the tool path optimization to ensure the meshing performances, which is further confirmed by FEM (finite element method). For the convenience of the FEM, the tooth surface points are calculated with an even distribution, and the calculation process is efficiently implemented with a closed-form solution. Based on the proposed method, the number (or total length) of the tool path is reduced, and the contact area is qualified. Both the simulation and cutting experiment are implemented to validate the proposed method.

Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.