Influence of graphite morphology on static and cyclic strength of
ferritic nodular cast iron

Gebhardt, Christian; Chen, Geng; Bezold, Alexander; Broeckmann, Christoph

doi:10.1051/matecconf/201816514014

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…In this section, we characterize the microstructure of the cast iron grade investigated with shape parameters. For this purpose, metallographic specimens are taken from fatigue samples investigated in [12]. The fatigue samples are taken from ingots with the geometry 100 x 100 x 200 mm.…”

Section: Metallographic Datamentioning

confidence: 99%

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Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Gebhardt¹,

Trimborn²,

Weber³

et al. 2020

Preprint

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The heterogeneous microstructure in metallic components results in locally varying fatigue strength. Metal fatigue strongly depends on size and shape of non-metallic inclusions and pores, commonly referred to as "defects". Nodular cast iron (NCI) contains graphite inclusions (nodules) whose shape and frequency influence the fatigue strength. Fatigue strength can be simulated by micromechanical finite element models. The drawback of these models are the large computational costs. Therefore, we employ a data-driven machine learning methodology. More precisely, we utilize the simplified residual neural network (SimResNet) which was recently introduced in [16] to predict fatigue strength from metallographic data.For the training, we use fatigue data which is simulated with a micromechanical model and the shakedown theorem. The micromechanical models are derived directly from micrographs of nodular cast iron, respectively. The application of SimResNet shows a good performance to predict fatigue strength by local microstructures of nodular cast iron. We show several test cases. The simplified character of SimResNet enables fast predictions of fatigue by microstructures, even in comparision to classical residual neural networks.

show abstract

Section: Metallographic Datamentioning

confidence: 99%

“…The commercial optimizer gurobi was used to solve the optimization problem [14]. The elastic and plastic constants were taken from [12]. Output of the simulation is a macroscopic load per micrograph where no failure due to accumulating plasticity occurs.…”

Section: Micromechanical Modelmentioning

confidence: 99%

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Gebhardt¹,

Trimborn²,

Weber³

et al. 2020

Preprint

Self Cite

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Multi-domain optimization of cast iron components in wind turbines

Weber

Broeckmann

Züch

et al. 2023

Forsch Ingenieurwes

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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.

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Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Gebhardt

Trimborn

Weber

et al. 2020

Mechanics of Materials

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Influence of graphite morphology on static and cyclic strength of ferritic nodular cast iron

Cited by 3 publications

References 32 publications

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Multi-domain optimization of cast iron components in wind turbines

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

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