Development of a simplified method for the lifetime prediction of profile rail guides under universal moment loads

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Profiled rail guides (PRG) transmit forces and torques in every direction, except of the travel direction, between relatively moving assemblies. The values of these forces are important input variables for the lifespan calculation of the PRG. Pitching and yawing moments lead to different rolling contact forces within a carriage track. With the "rolling contact related life calculation" (RCRL) this fact is taken into account, that leads to lifespans that are four times higher than with the conventional lifespan calculation method. For the determination of the rolling contact forces, a mathematical method was developed, that makes FE-Calculations redundant for the user. Previous experimental investigations, which are not finished yet, with pitching torques on the PRG have shown the possible applicability of the RCRL. What is striking, however, is the large variation in the distances travelled by the PRG around the lifespan calculated with the RCRL.PRG are usually used as a composite of a carriage with four wagons and two slides. The rigidities of the surrounding parts in combination with mounting and production tolerances as well as thermal expansions lead to constraint forces in the PRG, whereby the lifespan is affected in a negative way. Furthermore, load spectra that are used for lifespan calculations cannot always be predicted because they can often change with different machining operations.Therefore, the mentioned operating conditions of PRG and the individual damage process of every single PRG ought to be taken into account in this research project. With contactless sensors, the distance between the wagon and the slide should be measured continuously and in high resolution. With that and the previously developed calculation methods, the rolling contact forces can be calculated at real operating conditions and thus the RCRL can be done. In addition, the dynamic behaviour of the PRG ought to be analysed in order to realize a condition monitoring system that predicts an impending failure of the PRG with previously developed machine learning methods and/or wear models. For the realisation of the mentioned prediction methods, control integrated measurement and analysis functions have to be developed and, furthermore, lifespan investigations with variable loads have to be conducted for validation.The full potential of the PRG should be exploited much better, whereby the risk of the usage of PRG is reduced for the user and the maintenance interval is specified.