The following is a laboratory experiment designed to be performed by advanced-high school and beginning-college students. It is hoped that this experiment will create an interest in and further understanding of materials science. The objective of this experiment is to demonstrate that the microstructure of engineered materials is affected by the processing conditions in manufacture, and that it is possible to characterize the microstructure using image analysis with a computer. The principle of computer vision will first be introduced followed by the description of the system developed at Texas A&M University. This in turn will be followed by the description of the experiment to obtain differences in microstructure and the characterization of the microstructure using computer vision

Hough, C. L.

Massarweh, W.

Srinivasan, Malur N.

English

NASA Technical Reports Server

COMPUTER VLSlION IN 
MIICROSTRUCTU L ANALHISIS 
Department of Mechanical Engineering 
Texas A&M University 
College Station, Texas 77843-3123 
Telephone 409-845-14 17 
https://ntrs.nasa.gov/search.jsp?R=19920021028 2020-03-17T11:26:00+00:00Z
COMPUTER VISION IN MICROSTRUCTURAL mALYSIS 
M.N. Srinivasan, W. Massarweh, C.L.Hough 
Department of Mechanical Engineering 
Texas A&M University, College Station, TX 
KEY WORDS: computer, microstructure, solidification, c:asting, 
aluminum, cooling rate. 
PREREOUISITE KNOWLEDGE: This material could be demonsticated to 
a high school level materials science student, but would be more 
useful to a student at the college freshman level or above. This 
experiment is intended to convey to the student that properties are 
a consequenceof the microstructure and,therefore, characterization 
of microstructure is very important. 
OBJECTIVES: To demonstrate that the microstructure of 
engineered materials is affected by the processing conditions in 
manufacture, and that it is possible to characterize the 
microstructure using image analysis with a computer. 
EOUIPMENT AND SUPPLIES: 1) Laboratory heating or melting 
furnace, 2) molding sand and mold boxes, 3) aluminum-7% silicon 
alloy ingot, 4) stepped bar pattern, 5) chromel-alumel ther~nocouple 
and indicator, 6) metallographic sample preparation facil.ity, 7) 
personal computer with accessories as detailed in the paper. 
PROCEDURE: The principle of computer vision will first be 
introduced followed by the description of the system developed at 
Texas A&M University. This in turn will be followed by the 
description of the experiment to obtain differences in 
microstructure and the characterization of the microstructure using 
computer vision. 
The phenomenal advances in the computer and microelectronics 
industry have made computer vision within the affordable range of 
budget strapped users. A decent system could be assembled fo:r under 
$10,000 and the prices are still falling. In this paper, we will 
introduce one such system and its application in microstructural 
analysis. 
The application of computer vision in microstructural arlalysis 
varies in degree of sophistication from simple determination of 
phase ratio, up to sophisticated grain size and morphology 
analysis. For the sake of simplicity, we will suffice with the 
introduction of phase ratio determination as a simple but effective 
method for introducing imaging analysis methods in material science 
laboratory education. 
The microcomputer image acquisition and analysis system used 
in this experiment consists of two main modules: the optical 
module, and the image and analysis module. 
Thle optical module consists of a Nikon MeasureScope 20 
microscope equipped with the proper optical elements and lighting 
fixture. The microscope was also fitted with a lightweight black 
and white CCD (coupled charged device) camera. Depending on the 
specimen to be studied, the proper magnification and lighting 
method could be determined by trial and error. 
The image acquisition and analysis module is based around a 10 
P/1Hz AT-compatible 286 microcomputer, 1 Mbyte random access memory, 
and 40 Mbyte harddisk. A color monitoris optional. The computer is 
equipped with a (PCVISION) frame grabber and image analysis board. 
This board is capable of storing 2 images with a 512x512 
resolution, in addition to the capability of on-board image 
manipulation. The captured and processed images are displayed on 
a B&W TV monitor connected to the image board. 
The control and analysis software runs under DOS (disk 
operating system) and is composed of two different components. 
First, a menu driven softwarecomponentisused to grab, filter,and 
perform some basic measurements on the images, and finally store 
them in the image data base. With the system also comes a 
subsoutine library containing functions that ease the manipulation 
of images. Using those subroutines, the user can construct 
functions tailored specifically for a particular application. 
A typical digital image is composed of a collection of image 
element-s, each known as a pixel, which represents the average light 
intensity over a discrete portion of the total image. Both spatial 
resolution and light intensity resolution of the image depend on 
the hardware used, but a 512x512 pixel image with 256 gray level 
represents an average reasonably priced system. 
Phase Determination Procedure: 
As a first step, a digitized image of the specimen in question 
needs to be acquired by the computer. This could be obtained 
direct1.y through the camera mounted on the microscope from a 
metallographically prepared specimen. If a direct image is not 
feasible, a digitized image could be obtained from a still 
photograph scanned through a digitizer. It is very important in 
both options to preserve the contrast between the different phases 
in question. Weak contrast between the different phases will 
increases the difficulty of subsequent phase discrimination steps. 
To obtain phase discrimination in the image, it is crucial to 
map each phase in a different gray level intensity zone. Under 
ideal conditions, and if the contrast in the original image is 
good, a simple transformation of the image to a binary black and 
white by simplethresholdingwill suffice,see Figure 1. Thresholding 
is a simple operation whereby pixels with a value above a preset 
threshold are assigned a binary value of 1 (or white). If a pixel's 
intensity value is below the threshold it is assigned a value of 0 
(or black), as shown in Figure 2. 
In many cases, the image quality is not superior, or contrast 
between the phases in weak. In such cases, a number of alternatives 
can be used to improve the quality of the picture. In conditions 
where the imagesare noisy, a number of filtering methods could be 
used to obtain a better separation between the phases in the image, 
Such filters include convolution operators like smoothing, 
sharpening, or averaging filters. Other filters include 
morphological filters like dilation and erosion operators. The 
majority of those filters are included as part of basic: image 
processing package. 
Even in cases where phase discrimination exists but with 
minimal difference in the intensity, area measurements beeom& 
sensitive to the choice of a cutoff threshold value. In such 
situationsa method called histogram stretching is used to t?nhamce 
the contrast as shown in Figure 1. 
In the case of a binary phase material where a threshslded 
binary image is obtained, phase fraction determination can be 
obtained by calculating the fractions of black and white areas, A 
simple program that tests the image pixel by pixel keeping account 
of pixel count, then calculating the fraction will perform this 
task. 
Experiment Example: 
An experiment to determine the effect of section thickness 
(cooling or solidification rate) on microstructure of Al-7%Si was 
performed by senior level undergraduate students at Texas A&M 
University. 
A stepped bar pattern was used to make a composite casting of 
different thicknesses. The cooling curve in each section of the 
stepped bar, as the casting solidified, was recorded using a 
chromel-alumel thermocouple connected to a digital temperature 
readout. In Figure 3 the cooling curves thus obtained are shown, 
Each section of the casting was then metallographically polished 
and etched to reveal the microstructure. The percentage of alpha 
phase in each section was then determined using the image analysis 
procedure described earlier and was plotted against the casting 
thickness, as shown in Figure 4. It is seen that the percentage 
alpha decreases with increase in section thickness (or decrease in 
cooling rate). This implies that the amount of eutectic (in the 
interdendritic regions of alpha) follows the reverse trend. Since 
the eutectic contains brittle beta phase, it follows that the 
strength of the aluminum-7% silicon casting decreases with increase 
in section thickness. In Figure 5 is shown a typical 
photomicrograph with alpha (white) and alpha plus beta (dark) 
phases. The percentage alpha measured by the lineal analysis method 
for this case was about 65%, which compares favorably with the 
value of 67.5 % shown in Figure 4. 
REFERENCE: Flemings, M.C., Solidification Processimq, McGraw- 
Will, 1974, pp 134-172. 
SOURCES OF SUPPLIES: The casting materials, equipment and 
supplies can be obtained from companies listed in .the Thomas 
Register. The personal computer (PC-AT) can be purchased from any 
reliable source. For details of the image analysis system, please 
contact the authors. 
CONCLUDING REMARKS: The casting experiment has been chosen 
merely to demonstrate the need and capability to characzterize the 
microstructure. Image analysis can be useful for any engineered 
product, manufactured by processes like forging, rolling, welding, 
etc. 
ACKNOWLEDGEMENT: 
The experiments were performed by Brian Br~~hum, Brian 
Bidigare, Rafael Alvarez and Paul Bowers under the direction of the 
authors. The authors would like to thank these four former students 
for their dedicated work. 
Qurs 1 Image histogram stre 
Figure 2 Threshold selection in image histogram. 
F i g u r e  3 Casting cooling curves 




Computer vision in microstructural analysis

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