Research and development of gearboxes heavily relies on precise analysis and documentation of events of damage, serving as a foundation for continuous improvement. Particularly in the area of gearing, running tests are generally carried out to analyse the load-bearing capacity and failure occurring during operation in various damage criteria. These tests are documented and analysed using photographs of the tooth flank among other measurements. In these photographs a lot of valuable information is encrypted. Therefore, the primary focus must be on linking the image captured to the simulated load-bearing capacities across diverse applications and to use this connection for further tooth contact analysis.
A step in this direction was taken with the development of a method that integrates photogrammetric techniques to connect 2D image data with the 3D geometry of tooth flanks. This enables a consistent and efficient comparison of observed damage with simulation-based predictions, ensuring robust validation of load-bearing models.
To improve the possibilities of tooth contact analysis on bevel gears the photogrammetric method has been reworked and was combined with a TCA (Tooth Contact Analysis) module based on the concept presented in. To demonstrate the possibilities arising from this methodology, the gearbox assembly of bevel gears illustrates one possible application. Here the correct contact pattern position is an essential prerequisite for ensuring the designed load capacities.
By using the actual contact pattern position obtained from the photogrammetric measurement, the relative positions of pinion and gear are approximated by a quadratic regression analysis that calculates the optimal correction values for the gear mounting distances. This leads to the removal of subjective errors of the manual inspection process. Prototypic tests are carried out to validate the correct contact pattern position on the gear flanks. This way, the contact pattern correction in the gearbox assembly can be reliably and efficiently automated in the future.