ABSTRACT

Automatic identification and classification of objects being the

results of image recognition algorithms became more and more

popular in many aspects of human activity. On the other hand,

manual stereological methods and conventional image analyzer

are more often than not difficult and time-consuming tool to

obtain the informative data especially for complicated

microstructures. To solve these problems, a computer assisted

quantitative metallographic analysis was explored. The input

data for the proposed analysis was a set of digital 2D images of

metal microstructures of the technical aluminum cast Al-Si

alloys. Images were obtained by high quality cameras

embedded in optical microscopes. The objects of interest were

the precipitates of intermetallic phases of various morphological

shapes.

Traditionally, descriptions of microstructures have been based

on measurements of topological relationships between the three-

dimensonal space and two-dimensional microsections, such as

grain size, the average volume of particles, volume fraction,

size of particles in unit volume, etc. We consider these features

to be insufficient for the process of classification which permits

differentiation. Therefore, the computational methods of pattern

recognition have been applied to both the statistical particle

shape analysis and topological characterization of dendritic

structures. Several examples of designed and implemented

algorithms, including the measurements of compactness, scale

and rotation invariant moments, fractal dimension, convex hull,

lacunarity and many other parameters are presented. The key to

this quantitative analysis is the manner of interpretation of

aluminum alloys' planar microsections. It provides practical

techniques for extracting quantitative information from

measurements. It is these features that determine the mechanical

properties, and any advanced understanding of microstructure-

property relations requires their quantitative description.

The presented approach is aimed at designing a system for

identification and classification of microstructures occurring in

multiphase cast alloys. Image data representing diverse samples

was taken into investigation. Within each sample alloys’

features were determined based on a cast modeling process.

Due to the fact that the presence of specific microstructures

determines mechanical properties of cast alloys, an automated

image based classification system may be an invaluable tool for

developers of modern casting technology.

Keywords: computer vision, pattern recognition, image

processing, identification of metal phases, quantitative

metallography

1. INTRODUCTION

Polyphase metal alloys are still the most common structural

materials in the production of many goods. Increasing demands

regarding on one side, the quality of the products and on the

other saving costs and environmental friendly technology are

opening the wide fields for advanced methods of material

investigations. The starting points of prospective material

modification are always very well-known relationships C,T

↔ UP (where C- chemical composition, T- technology, UP –

utilizable properties) [1-6]. However, the another relationships

M ↔ UP (where: M- microstructure) represents a more

close and direct interaction which can be implemented into

physical or statistical material models [7]. The term ‘material

microstructure’ in material science means a 3D construction

composed of the particular elements differing in physical,

chemical and morphological properties. Light microscopy

investigations relate to the 2D representatives of the

microstructure constituents, revealed on the metallographic

plane cross sections with special preparation procedures. The

known stereology relationships allow direct matching of the 2D

quantitative global parameters for some microstructure models

to their 3D equivalents [8-10]. However, the general description

rules for the local features of the material constituents,

important from the point of view of its model behavior have not

been until yet established [11-13]. Especially, in the case of

concave dendritic particles an anticipated 2D ↔ 3D

morphology relationship can be univocal and even contradictory

(Fig.1).

The quantitative description of the local microstructure features

as shapes of particular elements is one of the most important

and difficult problems in microscope image analysis. A

Image Recognition, Identification and Classification Algorithms

in Cast Alloys Microstructure Analysis

Anna Romanowska-Pawliczek e-mail: anna@romanowska.pl

Department of Applied Computer Science and Modelling, Faculty of Metal Engineering and Industrial Computer

Science, AGH University of Science and Technology, Kraków, Poland

Aleksander Siwek Department of Applied Computer Science and Modelling, Faculty of Metal Engineering and Industrial Computer

Science, AGH University of Science and Technology, Kraków, Poland

Mirosław Głowacki

Department of Applied Computer Science and Modelling, Faculty of Metal Engineering and Industrial Computer

Science, AGH University of Science and Technology, Kraków, Poland

Małgorzata Warmuzek Foundry Research Institute, Kraków, Poland

computer assisted microscope image analysis has been explored

in order to solve this problem [8, 9, 14, 15].

In the multi component eutectic microregions during alloy

crystallization the phase constituents solidify into very

morphologically complicated forms. Commonly, concave solid

figures of a well-developed surface are found, sometimes

similar to either dendrite or fractal forms. On the basis of the

metallography knowledge, the characteristic morphology forms

are correlated to a particular phase constituent identified

univocally by its crystal structure [16, 17]. Local

crystallography phase identification is more time-consuming

than microscopic observation, thus the quantitative procedure

for classifying and discriminating observed morphology forms

will be a very useful examination tool.

shape phase crystal element

needle

β-AlFeSi

Al3Fe

Al7Cu2Fe

monoclinic

tetragonal

Al, Si, Fe,Cu

branch

Al3Ni

Al9FeNi

Al6Cu3Ni

orthorhombic

cubic

Al, Ni, Fe, Cu

Chinese script

α-AlFeSi

α-AlMnSi

Mg2Si

hexagonal

cubic

Al, Si, Fe,

Mn, Mg

Table 1. Morphology forms established as shape standards for

chosen intermetallic phases [16, 17]

Three morphology groups of the intermetallic phase precipitates

occurring in the eutectic solidifying in the Al alloys have been

chosen for the examinations. They have been named by with

using wide analogies as needles, branches, Chinese script. Each

of them is specific for a particular intermetallic phase group

(Tab. 1), therefore the establishment of the quantitative

coefficient allowing their separation on the microstructure

images will be considered an important progress in the solution

of the problem formulated above.

2. MATERIALS AND METHODS

Material preparation

The analyzed images represent microstructures of the technical

aluminum cast Al-Si alloys. The microstructure examinations

have been carried out on metallographic microsections, polished

with 0,25mm diamond suspensions and etched with 10%

NaOH.

Metallographic specimens of alloys' samples are subjected to

digestion, which reveals the overall picture of the structure and

enables identification of individual structural components.

Reagents reveal the structure of etched phases and grain

boundaries. Having a selective reagents’ stain or dissolving

certain components of the structure allows their identification.

The microstructure observation has been carried out by means

of the light metallographic microscope either Neophot 32 or

Axio OZm1. Microstructure pictures have been recorded in the

digital form as jpg files.

Image acquisition

Materials of examinations were series of the laboratory cast

multicomponent Al alloys. Microstructure images have been

revealed with the standard metallographic procedures on the

random plane cross sections. The pictures have been recorded in

RGB standard with 36bit color depths by means of the light

metallography microscope AxioObserver oZm combined with

high resolution Axiocam ICc3 camera of CCd basic resolution

2080x1540.

(a)

(b)

Figure 1. Results of the 3D reconstruction of the microstructure constituents shape: (a) sequence of the localized cross sections,

intermetallic phase in the Al-Si alloy [12], (b) FIB in SEM, flake graphite in the cast iron [13] (1- LM, plane cross section (FRI

microstructure archives), 2- SEM, deep etched cross section (FRI microstructure archives), 3- 3D reconstruction)

The quality of images depends on the use of light source, filters,

way of lighting the cross sections and choice of lenses mounded

in the microscope. Pictures of microstructures taken at various

magnifications include structural components (objects) which

differ from the matrix by luminance and chroma. The analysis

of such structures can be additionally impeded by uneven

illumination and low contrast. It requires balancing of global

and local histograms of images. Another problem is the lack of

continuity of grain boundaries and phase brightness due to the

heterogeneous matrix and the presence of noise in the image.

Finding the location of boundaries between the phases in the

image of the microstructure requires the use of various methods

of detection depending on the nature of the structure and the

lighting conditions.

Image analysis

Computer analysis of digital images requires finding a solution

to many problems. There is no methodology that allows to

approach each issue in the same way. For the analysis of

microstructures of aluminum alloys, it is necessary to carry out

a series of image operations, which result in the calculation of

parameters describing the structural components.

On the base of the previous works [7, 16, 17], the morphology

class of the observed microstructure constituents has been

arbitrarily established. The real material constituents,

represented on the microphotographs, i.e. specific image fields

recognized as particular intermetallic phase precipitate

representatives, have been attributed to the particular

morphology class according to their a priori visual pattern

recognition. The geometry of each morphology class of the

objects ought to be recognized and univocally described by

means of either one or another chosen group of the quantitative

coefficients.

The analyzed objects are characterized by different structural

shapes. It is important to describe shapes using parameters

(features) whose values do not depend on the microscope

magnification. From a mathematical point of view, it is

insufficient to describe the shape only by a single feature. From

the need to reduce the accuracy of shape description, it is

required to propose and design certain shape specifications.

These must be parameterless, easy to interpret and give values

reflecting differences in the shape of a specific type of structure.

For such purposes the use of so-called moments of inertia,

topological parameters and objects boundaries analyzes seems

promising. For each image these parameters can be calculated

by finding coherent components and skeletons of the images’

objects.

Description of shapes with a high degree of complexity causes

some of the parameters to be similar for various classes of

objects. In such cases, it is necessary to analyze a large sample

of images so that the classification is carried out by using the

average expected values.

Moments of inertia

In the process of image analysis the moments of inertia are very

widely used [18]. They describe the image content or its

distribution relative to the coordinate system. Moments reflect

the change of global and local geometry of the structure. By

analogy to the mechanics, image properties are characterized by

moments. Assuming a two-dimensional image as a continuous

function of the density distribution f(x, y), the moment of order

(p+q) for the entire image area Ω is defined as:

m pq=Ω

xp

yq

f x , ydxdy (1)

for p, q = 0,1,2, ... . As can be seen of Eq. (1) in the case of

image processing, the moment is a special feature of the

weighted average intensity of pixels. For binary images Eq. (1)

is converted into a discrete form. For simplicity it is assumed

that the image area is divided into squares of size 1x1, where

the value of the density function is constant. To an image

recorded in grayscale of brightness of pixels f(x,y), the moment

of ij is calculated as:

M ij=∑x

∑y

xiy

jf x , y

(2)

where x and y are the coordinates of successive pixels in the

image. For images of alloys microstructures analyzed in the

presented study, the moments were calculated according to

Eq. (2). Because the microstructures were digested with various

reagents and the light settings in the microscope were not

constant the binarization was performed. Specific moments of

binary image are: surface area, center of mass, orientation. For

example, the image property described by moments expressed

as:

M00 – the object's surface

M10 / M00 – coordinate xc of gravity center of an

object

M01 / M00 – coordinate yc of gravity center of an

object

Central moment pq for the image stored in the grayscale is

defined as:

µ pq=∑x

∑y

x− xcpy − yc

qf x , y

(3)

where f(x, y)=1 for pixels representing objects of a binary

image.

Central moments of the third row are calculated by formulas:

µ00= M 00

µ01= µ10= 0

µ11= M 11− xcM 01= M 11− ycM 10

µ20= M 20− xcM 10

µ02= M 02− ycM 10

µ21= M 21− 2xcM 11− ycM 202xc

2M 01

µ12= M 12− 2ycM 11− xcM 022yc

2M 10

µ30= M 30− 3xcM 202xc

2M 10

µ03= M 03− 3ycM 022yc

2M 01

(4)

Based on the above mentioned central moments Hu [19]

proposed the two-dimensional image set of invariant moments.

Seven independent moments can be used to identify and classify

objects regardless of their size, position and rotation. For this

purpose, normalized central moments ƞij are defined:

ƞij=µij

µ00

1i j

2 (5)

Moments regardless of scale, position and rotation Φi are

recorded as a combination of moments ƞij:

Φ1= ƞ20+ ƞ02

Φ2= (ƞ20+ ƞ02)2+ (2ƞ11)

2

Φ3= (ƞ30− 3ƞ12)2+ (3ƞ21− ƞ03)

2

Φ4= (ƞ30+ ƞ12)2+ (ƞ21+ ƞ03)

2

Φ5= (ƞ30− 3ƞ12)(ƞ30+ ƞ12)((ƞ30+ ƞ12)2− 3(ƞ21+ ƞ03)

2)

+ (3ƞ21− ƞ03)(ƞ21+ ƞ03)(3(ƞ30+ ƞ12)2− (ƞ21+ ƞ03)

2)

Φ6= (ƞ20− ƞ02)((ƞ30+ ƞ12)2− (ƞ21+ ƞ03)

2)

+ 4ƞ11(ƞ30+ ƞ12)(ƞ21+ ƞ03)

Φ7= (3ƞ21− ƞ03)(ƞ30+ ƞ12)((ƞ30+ ƞ12)2− 3(ƞ21+ ƞ03)

2)

− (ƞ30− 3ƞ12)(ƞ21+ ƞ03)(3(ƞ30+ ƞ12)2− (ƞ21+ ƞ03)

2)

(6)

3. RESULTS

Acquired images of specimens' sections were binaryzed with

the use of Otsu algorithm [20]. From obtained images 21

largest structures were chosen and classified by expert. Each

object was assigned to exactly one of the following classes:

needles, branches or Chinese script.

The aim of this study was to propose quantitative indicators

describing the phases of the microstructure that will allow you

to assign it to one of the above classes. Due to the shape of the

phases occurring in the various structures, the best parameters

should be such, that their value of which strongly depends on

the shape. For each analyzed object moments Φ1-Φ6 were

calculated. However, no combination of these parameters

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Figure 2. Overview of microscopic images of aluminum alloys at a magnification of 500x and images resulting from their processing. The

first row comprises of pictures of microstructures such as: (a) Chinese script, (b) branches and (c) needles. The binarization process results

in images presented in the second row (d, e, f). The last line contains images generated by segmentation process (g, h, i). Color indicates a

blue background while further objects are marked with contrasting colors. The resulting images include information on: the objects gravity

center position, the main axes, values of the first scale independent moment Φ1 and moments of inertia with respect to the main axis MC.

allowed classification of analyzed structural components.

Therefore, it became necessary to introduce a parameter

sensitive to the morphological differences of metal phases. In

this paper, a new morphological parameter moment of inertia of

objects according to their main axis was introduced. This value

was calculated by formula:

M C=∑x

∑y

d c

2x , yf x , y

(7)

where dc(x,y) is the distance from the object pixel with

coordinates (x,y) from its main axis, and f(x, y)=1 for object

pixels in the image binary.

The examples of structures from different classes and results of

their processing are presented on Fig. 2. The best results in

terms of their use in identifying the components of phase

microstructures give: first moment Φ1 and moment of inertia

with respect to the main axis of the objects MC.

Fig. 3 is a graphic summary of the results of measurements of

parameters Φ1 and MC for all 21 processed structures. Each

marker was labeled by color that corresponds to one of the class

assigned by expert: Chinese script (blue), branch (violet) or

needle (red). Selection of the largest structural elements for each

type of microstructures allowed to find a range of values for

different parameters and minimize the measurement error value.

The outlined areas, typical for ranges of values of particular

structures parameters are depicted in Fig. 3.

Figure 3. Summary of measurement results of parameters for

the largest structures of the phases such as: Chinese writing

(blue), branches (violet) and needles (red).

4. CONCLUSIONS

The aim of this study was to propose such quantitative

indicators that describe the phase microstructure and allow to

assign it to one of the three types of structures.

The results of the carried out quantitative microscope image

analysis have revealed that more complicated morphology

forms, present in the microstructure images of the cast Al alloys

cannot be univocaly described with only one geometry shape

factor used for tested images sets. Nevertheless, the presented

experiment has shown the possibility of particular morphology

class discrimination according to complex coefficients

combining particular geometry shape factor with one of the

binary image momentum. This result exposes to view the new

field of quantitative microstructure description as a very

important stage of the material model simulation and its

technical application.

The positive verification of the assumed attribution of the

morphology classes parameters to the particular microstructure

constituents provides a new tool of computer aided microscope

image interpretation.

Strong influence of both image quality (i.e. either metallography

cross section preparation or acquisition conditions) and

microscope magnification during present examinations suggests

the necessity of this procedure stage standardization.

ACKNOWLEDGEMENTS

This work has been supported by AGH University of Science

and Technology under Grant No. 18.18.110.034. It has also

been carried out with a financial support of the Polish Ministry

of Science and Higher Education under grant No. NN507

378735.

5. REFERENCES

[1] M. Hafiz &T.Kobayashi, Metallurgical factors affecting

impact toughness of eutectic Al-Si alloy, Z. Metallk. V89,

1998, pp.445-449.

[2] P.N Crepeau & S.D Antolovich, Structure property

relationships in aluminium alloy 339-T5: tensile behaviour

at room and elevated temperature, AFS Transactions,

1990, pp. 813-822.

[3] D. Lassance, Modelling of damage mechanisms in AlMgSi

alloys, Pres. Universitaire de l`UCL, Belgique, book,

2003.

[4] Yun Sun Han, Effect of intermetallic phases on the

mechanical properties of cast A356 alloy wheels, The 1st

International Forum on Strategic Technology, 12-20 oct

2006, pp.338-341.

[5] M. Nylen, U. Gustavsson, B. Hitchinson & A. Ortinas,

Mechanistic studies in clad aluminum alloys, Mat. Science

Forum, Vol. 217-222, 1996, pp.1703-1708.

[6] R.G. Hamerton, H. Cama & M.W. Meredith, Development

of the coarse intermetallic particle population in wrought

aluminium alloys during ingot castings and

thermomechanical processing, Mat. Science Forum, V331-

337, 2000, pp.143-154.

[7] M. Warmuzek, Microstructure aspect of the usable

properties formation of Al cast alloys (Mikrostrukturalny

aspekt kształtowania uŜytkowych własności odlewniczych

stopów Al), Odlewnictwo –Nauka i Praktyka, No.5-6,

2006, pp. 27-35.

[8] J.Ohser, 3D Images of Material Structures – Processing and

Analysis, Wiley VCH, 2009, pp. 79-148.

[9] L.Wojnar, Image Analysis Applications in Materials

Engineering, N. York, CRC Press, 1999.

[10] J.C. Jacquet & W. Hotz, Quantitative description of the

microstructure of aluminium foundry alloys, Cast Metals,

V4, 1992, pp. 200-225.

[11] H. Singh, A.M. Gokhale, Y. Mao, E.Tewari & A.K.

Sachdev, Reconstruction and quantitative characterization

of multiphase, multiscale three dimensional microstructure

of a cast Al-Si base alloy, Met.and Mat.Trans. B, V40B,

2009, pp. 859-870.

[12] A. Velichko, Quantitative 2D Characterization of Graphite

Morphologies in Cast Iron using FIB Microstructure

Tomography, Dissertation, Saarbrucken, 2008, pp. 52-55,

95-111.

[13] C.M. Dinnis, J.A. Taylor & .A.K. Dahle, Interaction

between iron, manganese and the Al-Si eutectic in Al-Si

alloys, Met.and Mat Trans.A, V37A, 2006, pp. 3283-3291.

[14] C. Sigli, H. Vichery & B. Grange, Computer assisted

metallurgy for non heat treated aluminum alloys, Mat.

Science Forum, V212-217, 1996, pp. 391-396.

[15] W.R. Cribb, Quantitative metallography of poly-phase

microstructures, Scripta Met. V12, 1978. pp.893-898.

[16] M. Warmuzek, Metallographic Techniques for Aluminium

and Aluminium Alloys, in: Metallography, V9, ASM

International, USA, 2004, pp. 711-752.

[17] J.E. Hatch, Aluminum – properties and physical

metallurgy, ASM International, Metals Park, Ohio 1993,

pp. 30-31.

[18] R. Mukundan & K.R. Ramakrishnan, Moment functions in

image analysis: theory and applications, e-book, 1998.

[19] M.K. Hu, Visual Pattern Recognition by Moment

Invariants, IRE Trans. Info. Theory, Vol. IT-8, 1962, pp.

179-187.

[20] Nobuyuki Otsu, A Threshold Selection Method from Gray-

Level Histograms, IEEE Transactions on Systems, Man &

Cybernetics 9 (1), 1979, pp. 62–66.