journal article·2025-09Open access

Assessment of fatigue crack initiation after overloads with substructure-sensitive crystal plasticity

Shahram Dindarlou, Gustavo M. Castelluccio

Abstract

Microstructure-sensitive fatigue initiation prognosis approaches typically assume uniform periodic loading and often overlook in-service overloads, which increase uncertainty and reduce life prediction accuracy. Similarly, certification efforts rarely evaluate experimentally the impact of different overloads due to the prohibitive costs. Therefore, predictive models that estimate overload effects on fatigue initiation damage without extensive experimental data are valuable to improve prognosis approaches. However, the literature lacks microstructure-sensitive approaches capable of assessing overload effects with models that simultaneously predict monotonic and cyclic responses without recalibration . This work presents a novel strategy to predict the effects of overloads on early cyclic damage by evaluating the refinement dislocation structures . A substructure-based crystal plasticity approach relies on independent parameterizations from monotonic and cyclic loading to predict overload responses, without requiring additional experiments. The model agreement with macroscale experiments was further validated by comparing dominant mesoscale structures after overloads in single- and poly-crystals for metals and alloys. The analysis also identified overload-resistant crystal orientations and demonstrated that overloads increase the likelihood of initiating fatigue cracks in low apparent Schmid factor grains under low-amplitude fatigue. We conclude by discussing the value of material-invariant mesoscale parameters to rank overloads effect for materials and loading conditions for which no experiments are available.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Fatigue and fracture mechanics · Material Properties and Failure Mechanisms · High Temperature Alloys and Creep

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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