scholarly journals Density-based clustering of crystal (mis)orientations and the orix Python library

2020 ◽  
Vol 53 (5) ◽  
pp. 1293-1298
Author(s):  
Duncan N. Johnstone ◽  
Ben H. Martineau ◽  
Phillip Crout ◽  
Paul A. Midgley ◽  
Alexander S. Eggeman
Keyword(s):  
Grain Boundaries ◽  
Clustering Algorithm ◽  
Three Dimensional ◽  
Orientation Relationships ◽  
Orientation Data ◽  
Orientation Space ◽  
Density Based Clustering ◽  
Orientation Mapping ◽  
Special Orientation ◽  
Clustering Approach

Crystal orientation mapping experiments typically measure orientations that are similar within grains and misorientations that are similar along grain boundaries. Such (mis)orientation data cluster in (mis)orientation space, and clusters are more pronounced if preferred orientations or special orientation relationships are present. Here, cluster analysis of (mis)orientation data is described and demonstrated using distance metrics incorporating crystal symmetry and the density-based clustering algorithm DBSCAN. Frequently measured (mis)orientations are identified as corresponding to similarly (mis)oriented grains or grain boundaries, which are visualized both spatially and in three-dimensional (mis)orientation spaces. An example is presented identifying deformation twinning modes in titanium, highlighting a key application of the clustering approach in identifying crystallographic orientation relationships and similarly oriented grains resulting from specific transformation pathways. A new open-source Python library, orix, that enabled this work is also reported.

Microstructure and Texture in Copper Sheets after Reverse and Cross Rolling

2005 ◽  
Vol 105 ◽  
pp. 309-314 ◽  
Author(s):  
M. Ostafin ◽  
Jan Pospiech ◽  
Robert A. Schwarzer
Keyword(s):  
Grain Boundaries ◽  
Crystal Orientation ◽  
High Spatial Resolution ◽  
Rolling Direction ◽  
Orientation Measurement ◽  
Orientation Data ◽  
Cross Rolling ◽  
X Ray ◽  
Orientation Mapping ◽  
The Individual

The objectives of this investigation are structural effects in electrolytic copper sheets which are caused by the change of the direction of rolling. Unidirectional, reverse as well as cross-rolling at 90° respectively at 45° to the precedent rolling direction have been applied down to final deformations as low as 80% reduction in thickness. Texture has been determined by ACOM (Automated Crystal Orientation Measurement, “Automated EBSD”) in the SEM and by X-ray pole figure measurement. The main benefits of ACOM are a high spatial resolution which enables the investigation of texture gradients from the mid plane to the surface of the sheet, and the visualization of the microstructure by crystal orientation mapping. In addition to local texture, statistical distributions of misorientations across grain boundaries and of S grain boundaries have been derived from the individual grain orientation data. The change of the path of plastic deformation induces a destabilization of the substructure which is formed during the primary step of unidirectional rolling. A distinct change of texture is found depending on the deformation process. In cross rolling, the b fiber changes into the unstable b90 fiber which almost disappears with progressive deformation along the new rolling direction.

Three-Dimensional Characterization of Morphology and Crystallography of a Gold Nanoisland Film

2021 ◽  
Vol 1016 ◽  
pp. 1778-1783
Author(s):  
Wan Guan Zhu ◽  
Gui Lin Wu ◽  
Tian Lin Huang ◽  
Soeren Schmidt ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Grain Boundaries ◽  
Three Dimensional ◽  
Mapping Technique ◽  
Transmission Electron ◽  
Orientation Mapping ◽  
Crystallographic Characteristics ◽  
3D Shapes ◽  
Nanoisland Film ◽  
Crystallographic Orientations

The morphological and crystallographic characteristics of noble metal nanoisland films play an important role in determining their properties, performance, and reliability. In this work we have applied a rapid three-dimensional orientation mapping technique in the transmission electron microscope (3D-OMiTEM) in the characterization of a gold nanoisland film. A volume of 200×1024×1024 nm3 has been analyzed, generating a 3D orientation map composed of more than 500 nanoislands and 7000 grains constituting the islands. The 3D shapes and sizes of individual islands and grains have been analyzed, revealing their true 3D morphological features and the correlation between the number of grains within individual islands and the size of the islands. The crystallographic orientations of the grains and the misorientations across the grain boundaries have been quantified, revealing a weak texture but a preferential presence of Σ3 and Σ9 grain boundaries in the gold nanoisland film.

scholarly journals On three-dimensional misorientation spaces

2017 ◽  
Vol 473 (2206) ◽  
pp. 20170274 ◽  
Author(s):  
Robert Krakow ◽  
Robbie J. Bennett ◽  
Duncan N. Johnstone ◽  
Zoja Vukmanovic ◽  
Wilberth Solano-Alvarez ◽  
...  
Keyword(s):  
Materials Science ◽  
Point Group ◽  
Three Dimensional ◽  
Orientation Relationships ◽  
Active Deformation ◽  
Orientation Data ◽  
Analysis Methodology ◽  
Mapping Techniques ◽  
Group Symmetries ◽  
Directing Attention

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance.

Representative reduction of crystallographic orientation data

2013 ◽  
Vol 46 (4) ◽  
pp. 960-971 ◽  
Author(s):  
Katja Jöchen ◽  
Thomas Böhlke
Keyword(s):  
Crystallographic Orientation ◽  
Electron Backscatter Diffraction ◽  
Three Dimensional ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
Orientation Data ◽  
Clustering Technique ◽  
Orientation Space ◽  
Data Points

Experimental techniques [e.g.electron backscatter diffraction (EBSD)] yield detailed crystallographic information on the grain scale. In both two- and three-dimensional applications of EBSD, large data sets in the range of 105–109single-crystal orientations are obtained. With regard to the precise but efficient micromechanical computation of the polycrystalline material response, small representative sets of crystallographic orientation data are required. This paper describes two methods to systematically reduce experimentally measured orientation data. Inspired by the work of Gao, Przybyla & Adams [Metall. Mater. Trans. A(2006),37, 2379–2387], who used a tessellation of the orientation space in order to compute correlation functions, one method in this work uses a similar procedure to partition the orientation space into boxes, but with the aim of extracting the mean orientation of the data points of each box. The second method to reduce crystallographic texture data is based on a clustering technique. It is shown that, in terms of representativity of the reduced data, both methods deliver equally good results. While the clustering technique is computationally more costly, it works particularly well when the measured data set shows pronounced clusters in the orientation space. The quality of the results and the performance of the tessellation method are independent of the examined data set.

Lattice imaging of precipitates in Al-Zn-Mg alloy

1986 ◽  
Vol 44 ◽  
pp. 412-413
Author(s):  
C. K. Wu
Keyword(s):  
Grain Boundaries ◽  
Homogeneous Nucleation ◽  
Alloy System ◽  
Mg Alloy ◽  
List Type ◽  
Lattice Structures ◽  
Type Number ◽  
Orientation Relationships ◽  
Lattice Imaging ◽  
The Matrix

The precipitation phenomenon in Al-Zn-Mg alloy is quite interesting and complicated and can be described in the following categories:(i) heterogeneous nucleation at grain boundaries;(ii) precipitate-free-zones (PFZ) adjacent to the grain boundaries;(iii) homogeneous nucleation of snherical G.P. zones, n' and n phases inside the grains. The spherical G.P. zones are coherent with the matrix, whereas the n' and n phases are incoherent. It is noticed that n' and n phases exhibit plate-like morpholoay with several orientation relationship with the matrix. The high resolution lattice imaging techninue of TEM is then applied to study precipitates in this alloy system. It reveals the characteristics of lattice structures of each phase and the orientation relationships with the matrix.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

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