| Abstract: | Material databases of plastics are becoming more and more the focus of applied science and commercial use in industry. Material properties of material manufacturers are often provided in publicly accessible material databases, which usually contain processing and mechanical properties under static loadings. Fatigue strength values are usually not accessible. The fatigue data for thermoplastics is of particular interest, as these materials have a particularly high lightweight construction potential and can be processed with a high degree of automation and reproducibility.Individual fatigue strength parameters for a specific material, environmental condition, geometry and loadings have been investigated in numerous publications. However, no work has been found in which fundamental interactions of different materials, environmental conditions, geometries and loadings on the course of the S/N-Curve have been investigated.In this paper, different effect relationships between temperature, filler type and filler content, fiber orientation and load ratio will be presented for the material Polypropylene (PP). A fatigue strength database of 11 different material manufacturers, from 71 different S/N-Curves with 606 tested samples, serves as a basis. The fatigue database enables a digital twin, which is used for the design of structural components to add a third dimension with artificial intelligence, and which is trained by an engineer. From the determined effect relationships, fatigue factors are to be derived and can be used to evaluate the fatigue strength of a component in the design process and to train the digital twin. The fatigue-strength values from the database also allow a statistical consideration of the slope k and the scattering of the S/N-Curve. The different S/N-Curves are transferred into a Haigh diagram, from which the functional course of the mean stress is determined. |
