Abstract

In practice, the analytical calculation of power springs often delivers only unsatisfactory results. The reason for this is the observed strong dependence on the manufacturing process as well as pronounced nonlinearities, such as the coil contact, the anisotropic elastic-plastic material behavior and the strong influence of friction. These manifold influencing variables and their feedback leads to a large number of prototype loops with correspondingly high development costs. In this paper, a methodology for the numerical calculation of power springs is presented in order to finally obtain a characteristic curve for the functional design and the corresponding load stresses for the fatigue assessment, which can be carried out using the FKM guideline "Springs and spring elements". Due to the prestressed installation situation, it is necessary to calculate the coiling process with subsequent installation in the housing. The plastic deformation and subsequent springback lead to a superposition of residual stresses and load stresses, which varies over the number of spring coils. As a result, the spring properties are significantly influenced by the underlying manufacturing process. The influence of the friction between the coils is shown in the paper as well as a direct comparison between measured characteristic curves and those predicted by the methodology shown in this paper. Only the integration of the manufacturing simulation into the design process makes it possible to realize an accurate prediction of the fatigue strength for power springs, since only in this way manufacturing residual stresses and load stresses for individual applications can be determined in advance of prototypes and then serve as input data for the evaluation of the degree of utilization according to the FKM guideline. The virtual spring must successfully meet all requirements in order to be manufactured in reality. This agile and, from the author's point of view, forward-looking development process makes it possible to face the challenges mentioned at the beginning and allows a detailed insight into variables that were previously difficult to access.