Browsing by Author "Dindarlou, Shahram"

Now showing 1 - 4 of 4
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    Assessment of fatigue crack initiation after overloads with substructure-sensitive crystal plasticity
    (Elsevier, 2025-09) Dindarlou, Shahram; Castelluccio, Gustavo M.
    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.
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    Modeling of overloads in cyclic loading
    (Cranfield University, 2022-09) Dindarlou, Shahram; Castelluccio, Gustavo M.
    While researchers have tried to predict mechanical response in cyclic loading, the mod- elling of overloads (monotonic loads among cycles) is still missing in the literature. This work presents a new crystal plasticity approach to predict the overload response. Even though crystal plasticity models have been available for decades, quantification of material parameters is still a matter of debate. Polycrystalline experimental results can normally be reproduced by multiple sets of parameters, raising concerns about the best parameterization to predict the grain-level response. Crystal plasticity parameters has been optimised by not only fitting experimental stress-strain curves but also independent prediction of mesoscale structures in this work. We employ a unique set of parameters with limited uncertainty to reproduce the mechanical response of FCC single- and poly- crystals in monotonic loading. We demonstrate that mesoscale parameters are material- invariant and can be used to model FCC metals with similar dislocation substructures such as for Cu, Ni and Al. Furthermore, the model is validated by comparing to experimental single- and poly-crystalline stress-strain curves and mesoscale dislocation substructure images. This work also innovates with a new optimization method to calibrate the proposed crystal plasticity model parameters. We estimated model parameters for Cu, Ni, and Al and showed that these parameters are applicable to predict stainless steel response without single crystal data. Finally, we used the model with optimised parameters to predict the overload response for Cu and NiCr alloy. The agreement with experiments for not only macroscopic re- sponse, but also mesoscale structures attributes shows robust prediction power of this work.
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    Optimization of crystal plasticity parameters with proxy materials data for alloy single crystals
    (Elsevier, 2024-01-28) Dindarlou, Shahram; Castelluccio, Gustavo M.
    Multiscale modeling approaches have demonstrated ample value in understanding, predicting, and engineering materials response. While increasing computational power has aided in modeling atomic behavior from first principles, modeling mesoscale mechanisms such as intergranular failure or crack initiation still rely strongly on correlative models. Crystal Plasticity models have been extensively used to relate process-property-structure in metallic materials including mesoscale effects such as texture, microplasticity, and failure variability. However, models still suffer from low predictive power at the grain scale, which leads to poor damage prognosis outside the experimental calibration set. In addition to model form error, mesoscale uncertainty is dominated by an inadequate model parameterization that arises from calibration exclusively to macroscopic experimental data. This work explores parameter uncertainty in Crystal Plasticity models and proposes a hybrid physic-based and numerical optimization approach to identify parameters associated to mesoscale strengthening in FCC metals and alloys. The strength and novelty of the approach rely on calibrating parameters independently using single-crystal and polycrystal stress–strain curves. We further demonstrate that multiple materials can be incorporated simultaneously into a single optimization algorithm to robustly quantify mesoscale material-invariant parameters. These values are then used to blindly predict the response of single- and poly-crystals engineering alloys. As a result, our approach mitigates modeling uncertainty by augmenting the data for calibration with single crystal experiments from different materials with similar dislocation structures (i.e., proxy materials). The results provide the basis for a robust parameterization of crystal plasticity models that can predict single- and poly-crystal responses for engineering alloys even in the absence of direct experimental data.
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    ItemOpen Access
    Substructure-sensitive crystal plasticity with material-invariant parameters
    (Elsevier, 2022-04-27) Dindarlou, Shahram; Castelluccio, Gustavo M.
    Even though crystal plasticity models have been available for decades, the quantification of material parameters is still a matter of debate. Polycrystalline experimental results can normally be reproduced by multiple sets of parameters, raising concerns about the best parameterization to predict the grain-level response. This work presents a novel physics-based crystal plasticity model based on mesoscale dislocation substructures, which are used to characterize material parameters independently. We employ a unique set of parameters with known uncertainty to reproduce the mechanical response of FCC single- and poly-crystals. We demonstrate that mesoscale parameters are material-invariant and can be used to model FCC metals with similar dislocation substructures such as for Cu, Ni and Al. Furthermore, the model is validated by comparing to experimental single- and poly-crystalline stress–strain curves and mesoscale dislocation substructure images. This novel modeling approach is intrinsically designed to predict the response of materials with similar dislocation substructures without the need of single crystal experimental data for calibration.