Texture Estimates by Means of Components / Abschätzung der Textur mittels Komponenten

Helming, K.; Schwarzer, Robert; Rauschenbach, Bernd; Geier, Stephan; Leiss, Bernd; Wenk, H R; Ullemeyer, Klaus; Heinitz, Joachim

doi:10.1515/ijmr-1994-850803

Cited by 5 publications

(11 citation statements)

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“…The actual selection of suited texture components must be conducted on the basis of the chosen deformation starting texture data (low carbon sheet steels in the current study) from which the deformation texture components can be fitted [54][55][56][57][58][59]. The fitting can be conducted using well-established standard methods [62][63][64][65]. Some experience in the field of recrystallization texture development is required for using the model since in the current formulation a certain set of possible recrystallization texture components from which the texture emerges must be prescribed.…”

Section: The Constrained Volume For a Heterogeneous Solid In Matrix N...mentioning

confidence: 99%

“…The model functions used in this work for fitting crystallographic textures were introduced by Matthies et al [60,61]. They have been discussed in detail in [60][61][62][63][64]. The model functions have an individual orientation density and full width at half maximum as a measure for the strength and scatter of the texture component they represent.…”

Section: Mathematical Formulation Of Texture Componentsmentioning

confidence: 99%

“…The deformation texture of a 80% cold rolled low carbon steel sheet was fitted from experimental data [54] using the texture component method in the version of Helming et al [62][63][64] assuming orthotropic symmetry. The degree of rolling deformation refers to the engineering thickness reduction ( h/ h 0 ) × 100% where h is the thickness reduction and h 0 the sheet thickness before cold rolling.…”

Section: Input Data and Parameters For The Recrystallization Simulationmentioning

confidence: 99%

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A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

Raabe

2006

Modelling Simul. Mater. Sci. Eng.

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The study presents an analytical model for predicting crystallographic textures and the final grain size during primary static recrystallization of metals using texture components. The kinetics is formulated as a matrix variant of the Johnson–Mehl–Avrami–Kolmogorov equation. The matrix form is required since the kinetic and crystallographic evolution of the microstructure is described in terms of a limited set of growing (recrystallizing) and swept (deformed) texture components. The number of components required (5–10) defines the order of the matrix since the kinetic coupling occurs between all recrystallizing and all deformed components. Each such couple is characterized by corresponding values for the nucleation energy and grain boundary mobility. The values of these parameters can be obtained by analytical or numerical coarse graining according to a renormalization scheme which replaces many individual grains which grow via recrystallization in a deformed texture component by a single equivalent recrystallization texture component or by fitting to experimental data. Each deformed component is further characterized by an average stored deformation energy. Each element of the kinetic matrix, reflecting one of the possible couplings between a deformed and a recrystallizing texture component, is then derived in each time step by a set of two differential equations. The first equation describes the thermally activated nucleation and growth processes for the expanded (free) volume for a particular couple of a deformed and a recrystallizing texture component and the second equation is used for calculating the constrained (real) volume for that couple which corrects the free volume for those portions of the deformation component which were already swept. The new method is particularly developed for the fast and physically based process simulation of recrystallization textures with respect to processing. The present paper introduces the method and applies it to the primary recrystallization of low carbon steels.

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Section: The Constrained Volume For a Heterogeneous Solid In Matrix N...mentioning

confidence: 99%

Section: Mathematical Formulation Of Texture Componentsmentioning

confidence: 99%

Section: Input Data and Parameters For The Recrystallization Simulationmentioning

confidence: 99%

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A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

Raabe

2006

Modelling Simul. Mater. Sci. Eng.

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“…The actual selection of suited components must be conducted on the basis of the chosen deformation starting texture data (low carbon sheet steels in the current study) from which the deformation texture components can be fitted [11][12][13][14][15][16]. The fitting can be conducted using well established standard methods [18][19][20].…”

Section: The Constrained Volume For a Heterogeneous Solid In Tensor N...mentioning

confidence: 99%

A Texture Component Model for Predicting Recrystallization Textures

Winning

Raabe

Brahme

2007

MSF

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“…Pole figure inversion is essential for determining the ODF. It can be solved, for example, by the so-called component method 16 , 17 , direct methods like the WIMV algorithm (named after its authors Williams, Imhof, Matthies and Vinel) 18 – 20 , or a series expansion method 11 , 13 , 21 , 22 . Fourier transformation methods are commonly used to solve a series expansion.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning

Meier

Ragunathan

Degener

et al. 2023

Sci Rep

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X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.

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Texture Estimates by Means of Components / Abschätzung der Textur mittels Komponenten

Cited by 5 publications

References 0 publications

A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

A Texture Component Model for Predicting Recrystallization Textures

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning

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