PSI - Issue 75

Laurent Dastugue et al. / Procedia Structural Integrity 75 (2025) 334–343 Laurent Dastugue et al. / Structural Integrity Procedia (2025)

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4.2. Consideration of Local SN-Curves Through Influence Factors Once the base SN-curve is established, local adaptations (see right image of fig. 5) are applied to account for component-specific influences. The following influence factors are incorporated to locally modify the fatigue strength: • Surface Finish Effects o Roughness Factor : Accounts for microscopic notches introduced by surface machining. o Boundary Layer Factor : Captures changes due to surface treatments or mechanical surface strengthening. • Probability of Survival o Adjusts the SN-curve based on the desired statistical confidence level. • Support Factor o Reflects the stress gradient effect, improving local fatigue strength in components with favorable stress distributions. These factors are applied as modifiers of the SN-curve, resulting in a locally valid SN-curve that captures the influence of both material behavior and geometric or processing-related characteristics. Further extension by incorporating additional influence factors as needed for specific applications are allowed. These may include: • Coating Factor: To account for surface coatings (e.g., paint, anodization) that influence fatigue performance. • Temperature Factor: To adjust fatigue strength for elevated or sub-zero operational temperatures. • Technological Size Factor: Reflects the scale effect, i.e., the influence of component size on fatigue strength due to inhomogeneities or statistical volume effects. The integration of an SN-curve generator based on the FKM Guideline not only simplifies the modeling process and reduces potential for user error, but also ensures a higher degree of automation, reliability, and standard compliance in fatigue simulations. 5. Full Integration – Details, Savings and Advantages The full integration of simulation workflows into modern solver architecture brings significant operational, computational, and storage-related advantages. Through lean data handling, it is now possible to perform highly efficient analyses, particularly for large-scale simulations such as modal-based transient dynamics jobs. This chapter outlines the mechanisms and benefits of full integration. One of the key outcomes of full integration is the elimination of redundant or manual tasks in simulation workflows. For instance, traditional workflows often require users to explicitly request stress output data and manage its storage. In the integrated framework, such operations are handled automatically (see Fig. 6). Stress data is generated and deleted dynamically, only when and where it is needed. This not only simplifies the user interaction but also ensures that unnecessary data is not stored, significantly minimizing disk usage.

Fig. 6. Automatic handling of prerequisites.