Endurance limit prediction for sulphur-alloyed spray-formed steels

Schumacher, J.; Bomas, H.; Zoch, H.‐W.

doi:10.1016/j.ijfatigue.2011.12.009

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Different methods are reported in the literature to predict the size of failure-critical inclusions in fatigue specimens based on metallographic investigations. [10,[20][21][22][23] Two different approaches are pursued to determine the inclusion sizes in cross sections. On the one hand, for several cross sections, the largest inclusion is determined [10,20,22] and on the other hand, all inclusions in a cross section, whose size exceeds a threshold value, are recorded.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For this reason, no predicted inclusion sizes are available as interim results. Schumacher et al [ 23 ] predicted the fatigue limit of specimens failed at manganese sulfides based on metallographic investigations. Again, the expected inclusion sizes were not available as interim results.…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, for several cross sections, the largest inclusion is determined [10,20,22] and on the other hand, all inclusions in a cross section, whose size exceeds a threshold value, are recorded. [10,[21][22][23] Thumser et al [20] compared the sizes of failure causing inclusions in fracture surfaces of fatigue specimens and the biggest inclusion sizes found in cross sections. The inclusions found in cross sections are much smaller than those found in the fracture surfaces.…”

Section: Introductionmentioning

confidence: 99%

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Calculation of the Fatigue Limit of High‐Strength Steel Specimens at Different Loading Conditions Based on Inclusion Sizes

Schumacher

Clausen

2021

steel research int.

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A calculation method for the fatigue limit of components made of high-strength steels that fail at inclusions is described. The method consists of two parts: first, the size of failure-critical inclusions is determined based on metallographic investigations. Then, the fatigue limit is determined based on the expected inclusion sizes using a fracture mechanics model. Influencing factors such as sample size, heat-treatment conditions, and mean stresses are considered. The model parameters are derived from fatigue test data collected over decades within the framework of several research projects. The dominant influencing factors are the inclusion size and the hardness of the steel matrix. Finally, the model is used to predict the fatigue limit of different specimens made of a bearing steel SAE 52 100. The sizes of failure-critical inclusions and fatigue limits calculated with the model are compared and evaluated on the basis of the inclusion sizes found in the samples' fracture surface and the experimentally determined fatigue limits.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Calculation of the Fatigue Limit of High‐Strength Steel Specimens at Different Loading Conditions Based on Inclusion Sizes

Schumacher

Clausen

2021

steel research int.

Self Cite

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Digital Methods for the Fatigue Assessment of Engineering Steels

Fliegener,

Rosenberger,

Luke

et al. 2024

Adv Eng Mater

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Engineering steels are used for a wide range of applications in which their fatigue behavior is a crucial design factor. The fatigue properties depend on various influencing factors such as chemical composition, heat treatment, surface properties, load parameters, microstructure, and others. During product development, various material characterization and qualification experiments are mandatory. For a faster and more cost‐efficient development, data driven methods (machine learning) promise to replace or to complement material testing by prediction of the fatigue strength. With an ontology‐based, semantically‐linked knowledge graph, representing the manufacturing history of the material, the influence of the parameters of the process chain on the resulting properties can be accounted for. Herein, it is shown how a fatigue database containing a wide range of materials is assembled from literature. After postprocessing and curation of the data, machine learning predictions of mechanical properties are discussed under multiple aspects. A domain ontology is defined, containing the relevant class definitions for the use case. After applying a data integration and mapping workflow, it is shown how the data can be systematically queried using knowledge graphs describing the manufacturing history of the materials.

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