The continuously rising demand for renewable energies leads to increased installations of wind turbines with higher power. While the current power-to-weight ratio of up to 20 metric tons of cast iron per megawatt is stagnating, cast iron components of modern wind turbines are facing new challenges in terms of weight, manufacturability, and castability. These challenges can be addressed by systematically using multi-domain optimization approaches to reduce component weight and increase local component utilization.In order to meet the requirements for modern cast iron components, this multi-domain approach must employ methods from casting simulation, micromechanical analysis, topology optimization, and strength assessment. Here, casting simulation is used to determine local microstructure descriptors, which are subsequently used in micromechanical shakedown analysis to estimate the local microstructure-dependent fatigue strength. In parallel to the fatigue strength estimation, topology optimization is performed iteratively in combination with a castability analysis. The component strength is evaluated using a strength assessment approach based on the previously determined local material properties in combination with the topology optimized component.In this study, the overall concept of the proposed multi-domain approach is presented and requirements for the application of such an approach are formulated. The use case of this study is a planet carrier of a wind turbine gearbox manufactured from austempered ductile cast iron ADI-GJS-1050‑6. For this use case, a weight reduction of 17% was achieved while maintaining the required stiffness, such that the microstructure variance along the component was significantly reduced. Furthermore, the potentials and limitations of the presented approach are outlined and discussed in the context of the design of heavy-section castings.

Understanding the fatigue behaviour of metallic materials is highly important when it comes to a reliable assessment of material degradation as a result of dynamic loading. Because the provision of such data is associated with great testing effort leading to increased time and cost requirements in terms of conventional methods, accelerated lifetime prediction methods are becoming increasingly important. However, the reduced number of fatigue specimens and tests complicates statistical validations of the obtained results. In this contribution, combinatorial approaches are used to estimate both lifetime prediction bands and virtually-determined S-N curves with a reduced number of specimens, displaying the material-related scatter due to microstructural inhomogeneities. In addition, a variable energy dissipation factor based on cyclic deformation curves is presented, which enables evaluation of materials that exhibit more pronounced scatter, for instance cast materials. An in situ evaluation of the cyclic deformation curves is provided via integration of non-destructive testing methods into the testing rig. Unalloyed SAE 1045 steel, low-alloyed 20MnMoNi5-5 steel, and the cast material EN-GJS-1050-6 are investigated in this research, as these materials posses gradually increasing complexity regarding their respective microstructures.