PREDICTING 3D GRAIN SIZE FROM 2D IMAGE ANALYSIS: AN EXPLORATION OF STEREOLOGICAL CORRECTIONS IN SILTY SEDIMENT

2020 ◽  
Author(s):  
Autumn V. Roche ◽  
◽  
Lily S. Pfeifer ◽  
Gerilyn S. Soreghan ◽  
Michael J. Soreghan
Keyword(s):  
Grain Size ◽  
Image Analysis ◽  
Silty Sediment

Effect of the Two-Steps Sintering in the Microstructure of Ultrafine Alumina

2008 ◽  
Vol 591-593 ◽  
pp. 611-615
Author(s):  
Adriana Scoton Antonio Chinelatto ◽  
Milena K. Manosso ◽  
Elíria Maria Jesus Agnolon Pallone ◽  
Adilson Luiz Chinelatto
Keyword(s):  
Heat Treatment ◽  
Grain Size ◽  
Image Analysis ◽  
Microstructural Analysis ◽  
Analysis Program ◽  
Constant Heating Rate ◽  
Heating Curve ◽  
Final Microstructure ◽  
Constant Heating ◽  
Scanning Electron

The control of the heating curve to manipulate microstructure during sintering is a way that has being studied and it presents advantages such as simplicity and economy. In this work, it was studied the sintering in two-steps of a commercial ultrafine alumina. For this, the alumina power was deagglomerated in milling ball and the specimens for sintering were pressed. Sintering was performed in a dilatometer, with constant heating rate of 15°C/min up to 1500°C. By these results, heat treatment temperatures for two-step sintering were defined. The sintering specimens were characterized through the apparent density measures using Archimedes method, the grain size measures using image analysis program and microstructural analysis using a scanning electron microscope. The results showed that the two-step sintering influence in the development of the final microstructure and permit the control of the grain size and density.

scholarly journals A Very Simple Method for Quickly Making Large Numbers of Measurements on Micrographs

2009 ◽  
Vol 17 (3) ◽  
pp. 50-53
Author(s):  
Ron Anderson
Keyword(s):  
Thin Film ◽  
Grain Size ◽  
Image Analysis ◽  
Integrated Circuit ◽  
Ceramic Substrate ◽  
Raw Material ◽  
Material Particle ◽  
Simple Method ◽  
Plan View ◽  
Large Computer

It has been in the back of my mind to write this up for MT since I retired from a certain large computer company. Inasmuch as Paul's article above is a perfect lead-in, there is no time like the present. Our lab supported a semiconductor integrated circuit and a ceramic substrate manufacturing facility. We were continually required to measure circuit line widths on plan-view specimens and layer thicknesses on cross-section specimens for both semiconductor and ceramic substrate specimens and we were often asked to determine thin film grain size and ceramic raw material particle size data. A large number of measurements were required for each specimen to guarantee statistically sound data. We had image analysis software available that we used whenever we could, but often found that measuring things on a system using grey-level image analysis as input simply did not work. This is especially true for thin film grain size determination when using diffraction contrast TEM images for input.

scholarly journals Grain Sizing of Anodized Aluminum by Color Image Analysis

1997 ◽  
Vol 5 (4) ◽  
pp. 17-19
Author(s):  
Sylvain Laroche ◽  
Clement Forget
Keyword(s):  
Grain Size ◽  
Image Analysis ◽  
Human Error ◽  
Color Image ◽  
Polarized Light ◽  
Optical Microscope ◽  
Anodized Aluminum ◽  
Manual Task ◽  
Color Image Analysis ◽  
Rolled Aluminum

Grain size characterization in Aluminum alloys can be correlated with thermo-mechanical processing properties. In order to predict the processing characteristics of these alloys under certain combinations of strain, deformation and temperature, the metallographic measure of the grain size can be used. Most of the technigues that have been proposed so far do not provide reliable and reproducible quantitative metallographic measurements of the grain size due to human error. Considering that this manual task is also tedious to perform, a general color image analysis algorithm is proposed to automate the characterization process using an optical microscope with polarized light. This algorithm was tested on several ingots and on rolled aluminum samples. The results show robustness in several conditions, even when the grains can barely be seen by a human operator Other image analysis techniques have been proposed but where judged too slow or too complex, particularly when gathering data over several fields. Time constraints specific to industrial seffings were taken into account when implementing a new algorithm.

Effects of resolution on the measurement of grain ‘size’

1985 ◽  
Vol 49 (353) ◽  
pp. 539-546 ◽  
Author(s):  
R. Dearnley
Keyword(s):  
Grain Size ◽  
Image Analysis ◽  
Grain Boundary ◽  
Surface Area ◽  
Unit Volume ◽  
Fractal Dimensions ◽  
Size Analysis ◽  
Automatic Image Analysis ◽  
Fine Grained ◽  
Kinetics Of

AbstractMeasurements of fine-grained dolerites by optical automatic image analysis are used to illustrate the effects of magnification and resolution on the values obtained for grain ‘size’, grain boundary length, surface area per unit volume, and other parameters. Within the measured range of optical magnifications (× 26 to × 3571) and resolutions (1.20 × 10−3 cm to 8.50 × 10−6 cm), it is found that the values of all grain parameters estimated by chord size analysis vary with magnification. These results are interpreted in terms of the concepts of ‘fractal dimensions’ introduced by Mandelbrot (1967, 1977). For some comparative purposes the fractal relationships may be of little significance as relative changes of size, surface area, and other parameters can be expressed adequately at given magnification(s). But for many studies, for instance in kinetics of grain growth, the actual diameter or surface area per unit volume is an important dimension. The consequences are disconcerting and suggest that it may be difficult in some instances to specify the ‘true’ measurements of various characteristics of fine-grained aggregates.

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