Effective properties of nodular cast-iron: A multi-scale computational approach

Carazo, Fernando D.; Giusti, Sebastián M.; Boccardo, Adrián Dante; Godoy, Luis A.

doi:10.1016/j.commatsci.2013.09.044

Cited by 19 publications

(18 citation statements)

References 33 publications

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“…This effect can be explained as the system represented in Equation (2) becomes indeterminate as the curve degree used is greater than the number of points that the contour has, generating spurious Table 1 shows the generated models, where geometric evolution it can be seen as the Bézier curve degree increases. In relation to the models obtained using a fixed Bézier curve degree for all of the nodules, it can be seen that the curves that are obtained with Bézier degrees of 4 are curves with high CSF est values and high E r values, therefore do not approximate adequately to the geometry of the digital contours; on the other hand, as the curve degree increases (degree 10), the curves approximate to the contours obtained digitally preserving the smoothness that characterizes them, unlike the models that are obtained with commercial img2CAD software and the models that are obtained by another methodologies [11,14,16], where no smooth algorithms has been applied.…”

Section: Geometric Comparison Of the Obtained Modelsmentioning

confidence: 88%

“…Table 1 shows the models obtained from four nodules (A, B, C and D) using the three different methods mentioned, besides the CSF est and E r values for each graphite nodule are shown, where the maximum errors are highlighted in bold. Regarding the models that are generated using a fixed Bézier degree for all the nodules, three different models were created with the following degrees: 4, 10, and 18. high CSFest values and high Er values, therefore do not approximate adequately to the geometry of the digital contours; on the other hand, as the curve degree increases (degree 10), the curves approximate to the contours obtained digitally preserving the smoothness that characterizes them, unlike the models that are obtained with commercial img2CAD software and the models that are obtained by another methodologies [11,14,16], where no smooth algorithms has been applied. Table 1.…”

Section: Geometric Comparison Of the Obtained Modelsmentioning

confidence: 94%

“…The different DCIs mechanical properties, such as, high values of tensile strength and Young's modulus, among others [7], are some of the reasons that make DCIs very versatile for different engineering fields such as the automotive industry, mining industry, among others [8,9]. To perform the analysis of the mechanical properties, it is necessary to use computational tools, such as the Finite Element Analysis (FEA), which allows for relating the mechanical behavior with the microstructural properties of the DCI [10,11]. In order to obtain adequate results through the FEA, it is necessary to develop models that represent the geometry of the DCI as closely as possible to the real geometries (or to that established by studies concerning nodule sphericity), since models with incorrect approximations, which can show very pronounced or irregular graphite nodules contours, can generate stress concentrators that will lead in practice to an incorrect estimation of the mechanical properties in the FEA simulation.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the work of Carazo et al [11], where they predicted Young's effective modulus and Poisson's ratio of a DCI based on a multi-scale computational model. The models were generated using image processing techniques to extract the coordinates of the pixels representing the graphite nodules contours, which are used to generate the model for the FEA.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm-Based Optimization Methodology of Bézier Curves to Generate a DCI Microscale-Model

et al. 2017

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Abstract:The aim of this article is to develop a methodology that is capable of generating micro-scale models of Ductile Cast Irons, which have the particular characteristic to preserve the smoothness of the graphite nodules contours that are lost by discretization errors when the contours are extracted using image processing. The proposed methodology uses image processing to extract the graphite nodule contours and a genetic algorithm-based optimization strategy to select the optimal degree of the Bézier curve that best approximate each graphite nodule contour. To validate the proposed methodology, a Finite Element Analysis (FEA) was carried out using models that were obtained through three methods: (a) using a fixed Bézier degree for all of the graphite nodule contours, (b) the present methodology, and (c) using a commercial software. The results were compared using the relative error of the equivalent stresses computed by the FEA, where the proposed methodology results were used as a reference. The present paper does not have the aim to define which models are the correct and which are not. However, in this paper, it has been shown that the errors generated in the discretization process should not be ignored when developing geometric models since they can produce relative errors of up to 35.9% when an estimation of the mechanical behavior is carried out.

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Section: Geometric Comparison Of the Obtained Modelsmentioning

confidence: 88%

Section: Geometric Comparison Of the Obtained Modelsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm-Based Optimization Methodology of Bézier Curves to Generate a DCI Microscale-Model

et al. 2017

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“…They are oversimplified when graphite morphologies should be investigated. RVE models obtained directly according to micrographs are shown in [24,[34][35][36]. Lastly, a 3D UCM of a single inclusion has been developed in [37] by performing metallographic investigation of up to 100 slices of the same cross section.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Abstract. High silicon alloyed nodular cast iron consists of a purely ferritic matrix and graphite nodules, mainly. Varying wall thicknesses and manufacturing conditions result in different graphite morphologies throughout a structural component. From an experimental point of view, axial fatigue and tensile tests were carried out on specimens with differently degraded graphite. From a numerical point of view, the microstructure has been modelled using a finite element (FE) approach with representative volume elements (RVE). The RVE models were built according to micrographs of fatigue specimens. The generated RVEs determine effective material properties through elasto-plastic homogenization and were subsequently analysed using a shakedown approach. In shakedown theory, the material re-enters the elastic regime after a few cycles of initial plastic deformation. This work uses the shakedown theorem to derive a lower bound estimation of the endurance limit from a non-incremental simulation. Here, the material has to be modelled elastic-perfectly plastic. The major challenge in modelling nodular cast iron is to determine suitable material parameters for the graphite and ferrite phase, revealed by parameter studies on the static and cyclic model. By using reasonable material parameters, fundamental effects, observed in the fatigue tests, were reproduced on the model level.

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Micromechanical characterization of ferritic ductile cast iron by using instrumented indentation and atomic force microscopy

Fernandino

Daga

Boeri

2020

Mat Design & Process Comms

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The aim of this investigation is to characterize the mechanical properties at the microstructural level in ferritic ductile iron. The analysis involves microstructural characterization, nanoindentation testing, atomic force microscopy analysis, and the application of an inverse algorithms proposed in the literature. The results show that, because of microsegregation, different regions of a single‐phase ferritic matrix have different elastic‐plastic behavior. The methodology developed in this work becomes useful to evaluate the mechanical properties along the metallic matrix of other ductile iron microstructures.

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Effective properties of nodular cast-iron: A multi-scale computational approach

Cited by 19 publications

References 33 publications

Genetic Algorithm-Based Optimization Methodology of Bézier Curves to Generate a DCI Microscale-Model

Genetic Algorithm-Based Optimization Methodology of Bézier Curves to Generate a DCI Microscale-Model

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

Micromechanical characterization of ferritic ductile cast iron by using instrumented indentation and atomic force microscopy

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