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  • Open Access


    Image Processing/Machine-Learning for Auto-Labeling of Steel Images on Present Microstructures

    Dmitry S. Bulgarevich1,*, Susumu Tsukamoto1, Tadashi Kasuya2, Masahiko Demura1, Makoto Watanabe1,3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 122-122, 2019, DOI:10.32604/icces.2019.05271

    Abstract The microstructure of steel greatly determines its mechanical properties/performance and holds information on chemical composition and processing history. Therefore, quantitative analysis of optical or SEM images on formed microstructure phases is one of the primary interests for metallurgy. So far, such analyses in laboratories are done manually by experts and are very time consuming. However, with modern microscopy techniques of automated image acquisitions over the large imaging areas and even by using of sample slicing for three-dimensional imaging, the amount of image data could be overwhelming for manual examinations. In this respect, there is a… More >

  • Open Access


    Adaptive Multi-Scale Computational Modeling of Composite Materials

    P. Raghavan1, S. Ghosh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.5, No.2, pp. 151-170, 2004, DOI:10.3970/cmes.2004.005.151

    Abstract This paper presents an adaptive multi-level computational model that combines a conventional displacement based finite element model with a microstructural Voronoi cell finite element model for multi-scale analysis of composite structures with non-uniform microstructural heterogeneities as obtained from optical or scanning electron micrographs. Three levels of hierarchy, with different resolutions, are introduced in this model to overcome shortcomings posed by modeling and discretization errors. Among the three levels are: (a) level-0 of pure macroscopic analysis; (b) level-1 of macro-micro coupled modeling, used for signaling the switch over from macroscopic analyses to pure microscopic analyses; and More >

  • Open Access


    Modeling of Random Bimodal Structures of Composites (Application to Solid Propellants): II. Estimation of Effective Elastic Moduli

    V.A. Buryachenko1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.85, No.5, pp. 417-446, 2012, DOI:10.3970/cmes.2012.085.417

    Abstract We consider a linearly elastic composite medium, which consists of a homogeneous matrix containing a statistically homogeneous set of multimodal spherical inclusions modeling the morphology of heterogeneous solid propellants (HSP). Estimates of effective elastic moduli are performed using the multiparticle effective field method (MEFM) directly taking into account the interaction of different inclusions. Because of this, the effective elastic moduli of the HSP evaluated by the MEFM are sensitive to both the relative size of the inclusions (i.e., their multimodal nature) and the radial distribution functions (RDFs) estimated from experimental data, as well as from More >

  • Open Access


    Modeling of Random Bimodal Structures of Composites (Application to Solid Propellants): I. Simulation of Random Packs

    V.A. Buryachenko1,2, T.L. Jackson2,3, G. Amadio3

    CMES-Computer Modeling in Engineering & Sciences, Vol.85, No.5, pp. 379-416, 2012, DOI:10.3970/cmes.2012.085.379

    Abstract We consider a composite medium, which consists of a homogeneous matrix containing a statistically homogeneous set of multimodal spherical inclusions. This model is used to represent the morphology of heterogeneous solid propellants (HSP) that are widely used in the rocket industry. The Lubachevsky-Stillinger algorithm is used to generate morphological models of HSP with large polydisperse packs of spherical inclusions. We modify the algorithm by proposing a random shaking procedure that leads to the stabilization of a statistical distribution of the simulated structure that is homogeneous, highly mixed, and protocol independent (in sense that the statistical More >

  • Open Access


    From Ordered to Disordered: The Effect of Microstructure on Composite Mechanical Performance

    L.B. Borkowski1, K.C. Liu1, A. Chattopadhyay1

    CMC-Computers, Materials & Continua, Vol.37, No.3, pp. 161-193, 2013, DOI:10.3970/cmc.2013.037.161

    Abstract The microstructural variation in fiber-reinforced composites has a direct relationship with its local and global mechanical performance. When micromechanical modeling techniques for unidirectional composites assume a uniform and periodic arrangement of fibers, the bounds and validity of this assumption must be quantified. The goal of this research is to quantify the influence of microstructural randomness on effective homogeneous response and local inelastic behavior. The results indicate that microstructural progression from ordered to disordered decreases the tensile modulus by 5%, increases the shear modulus by 10%, and substantially increases the magnitude of local inelastic fields. The More >

  • Open Access


    Stochastic Macro Material Properties, Through Direct Stochastic Modeling of Heterogeneous Microstructures with Randomness of Constituent Properties and Topologies, by Using Trefftz Computational Grains (TCG)

    Leiting Dong1,2, Salah H. Gamal3, Satya N. Atluri2,4

    CMC-Computers, Materials & Continua, Vol.37, No.1, pp. 1-21, 2013, DOI:10.3970/cmc.2013.037.001

    Abstract In this paper, a simple and reliable procedure of stochastic computation is combined with the highly accurate and efficient Trefftz Computational Grains (TCG), for a direct numerical simulation (DNS) of heterogeneous materials with microscopic randomness. Material properties of each material phase, and geometrical properties such as particles sizes and distribution, are considered to be stochastic with either a uniform or normal probabilistic distributions. The objective here is to determine how this microscopic randomness propagates to the macroscopic scale, and affects the stochastic characteristics of macroscopic material properties. Four steps are included in this procedure: (1)… More >

  • Open Access


    Applications of the Phase-Coded Generalized Hough Transform to Feature Detection, Analysis, and Segmentation of Digital Microstructures

    Stephen R. Niezgoda1, Surya R. Kalidindi1,2

    CMC-Computers, Materials & Continua, Vol.14, No.2, pp. 79-98, 2009, DOI:10.3970/cmc.2009.014.079

    Abstract The generalized Hough transform is a common technique for feature detection in image processing. In this paper, we develop a size invariant Hough framework for the detection of arbitrary shapes in three dimensional digital microstructure datasets. The Hough transform is efficiently implemented via kernel convolution with complex Hough filters, where shape is captured in the magnitude of the filter and scale in the complex phase. In this paper, we further generalize the concept of a Hough filter by encoding other parameters of interest (e.g. orientation of plate or fiber constituents) in the complex phase, broadening More >

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