scholarly journals A New Library Program for Generating Augmented Jacobi Polynomials for Texture Calculations

1981 ◽  
Vol 4 (3) ◽  
pp. 143-151 ◽  
Author(s):  
J. I. Ohsugi ◽  
T. Fujii
Keyword(s):  
Texture Analysis ◽  
Spatial Orientation ◽  
Jacobi Polynomials ◽  
Three Dimensional ◽  
Orientation Distribution ◽  
Distribution Maps ◽  
Library Program

A new library program for generating augmented Jacobi polynomials for texture analysis is presented. By using this program, the spatial orientation distribution maps for the three-dimensional texture analysis can be produced.

Improvements in the Quantitative Evaluation of Three-Dimensional Texture. II. The Pole-Figure-Multiplying Method

1995 ◽  
Vol 28 (5) ◽  
pp. 532-533 ◽  
Author(s):  
L.-G. Yu ◽  
H. Guo ◽  
B. C. Hendrix ◽  
K.-W. Xu ◽  
J.-W. He
Keyword(s):  
Distribution Function ◽  
Texture Analysis ◽  
Pole Figure ◽  
Quantitative Evaluation ◽  
Orientation Distribution Function ◽  
Three Dimensional ◽  
Orientation Distribution ◽  
Simple Method ◽  
Pole Figures

A new simple method is proposed for determining the orientation distribution function (ODF) for three-dimensional texture analysis in a polycrystal based on the reality that the accuracy of an ODF is dependent on both the accuracy of each measured pole figure and the number of pole figures.

scholarly journals A New Library Program for Texture Calculations

1977 ◽  
Vol 2 (4) ◽  
pp. 225-241 ◽  
Author(s):  
F. Wagner ◽  
C. Esling ◽  
R. Baro
Keyword(s):  
Texture Analysis ◽  
Computing Time ◽  
Three Dimensional ◽  
Great Precision ◽  
Memory Space ◽  
Pole Figures ◽  
Library Program ◽  
Short Time ◽  
And Storage ◽  
Good Agreement

A new library program which allows the calculation and storage of the numerical tables necessary for a three-dimensional texture analysis is proposed. Its main characteristics are:–possibility of selecting the values to be stored according to the desired microscopic and macroscopic symmetries as well as to the step of exploration of the pole figures;–possibility of choosing the quantity of information to be stored for obtaining, in the further three-dimensional analysis, a good agreement between the computing time and the memory space; and–great precision of the stored values and short time of calculation due to the use of new and optimized aigorithms.

scholarly journals Evaluation by Computer Simulation of Certain Errors in the Three Dimensional Texture Analysis

1978 ◽  
Vol 3 (1) ◽  
pp. 27-36
Author(s):  
M. Humbert ◽  
F. Wagner ◽  
R. Baro
Keyword(s):  
Computer Simulation ◽  
Distribution Function ◽  
Texture Analysis ◽  
Pole Figure ◽  
Orientation Distribution Function ◽  
Three Dimensional ◽  
Orientation Distribution ◽  
Experimental Errors

The influence of certain experimental errors in pole-figure determination on the accuracy of calculated coefficients of the orientation distribution function has been analyzed.

scholarly journals Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yue Li ◽  
Xuyang Zhou ◽  
Timoteo Colnaghi ◽  
Ye Wei ◽  
Andreas Marek ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Chemical Species ◽  
Atom Probe ◽  
Local Order ◽  
Face Centered Cubic ◽  
Ordered Structure ◽  
Ordered Structures ◽  
Distribution Maps ◽  
Structure Recognition ◽  
Fcc Alloys

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

scholarly journals Texture Analysis of a Zinc Layer on a Steel Substrate Using Neutron Diffraction

1993 ◽  
Vol 21 (2-3) ◽  
pp. 71-78
Author(s):  
H.-G. Brokmeier
Keyword(s):  
Distribution Function ◽  
Neutron Diffraction ◽  
Texture Analysis ◽  
Orientation Distribution Function ◽  
Steel Substrate ◽  
Orientation Distribution ◽  
Pole Figures ◽  
Iron And Zinc

This paper describes the application of neutron diffraction to investigate the texture of a zinc layer 8 μm in thickness. In a nondestructive way both the texture of the zinc layer as well as the texture of the steel substrate were studied. Therefore, pole figures of iron ((110), (200) and (211)) and of zinc ((0002), (101¯0), (101¯1); and (101¯3)/(112¯0)) were measured; additionally the orientation distribution function of iron and zinc were calculated.

scholarly journals Application of Anisotropic Texture Components

1997 ◽  
Vol 28 (3-4) ◽  
pp. 181-195 ◽  
Author(s):  
Th. Eschner ◽  
J.-J. Fundenberger
Keyword(s):  
Aluminum Alloy ◽  
Texture Analysis ◽  
Series Expansion ◽  
Texture Component ◽  
Tensile Testing ◽  
Texture Evolution ◽  
Orientation Distribution ◽  
Distribution Functions ◽  
Weighted Sum ◽  
Recent Developments

The description of textures in terms of texture components is an established conception in quantitative texture analysis. Recent developments lead to the representation of orientation distribution functions as a weighted sum of model functions, each corresponding to one anisotropic texture component. As was shown previously, an adequate texture description is possible with only a very small number of anisotropic texture components. As a result, textures and texture changes can be described by a small number of vivid parameters and their variations, namely by volume parts, half widths and ideal orientations.The texture of a tensile tested commercial aluminum alloy was investigated by decomposition into anisotropic components. The texture evolution during tensile testing is represented by the corresponding changes of the component parameters and compared with results from an iterative series expansion analysis.

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