image quality analysis: a new method of characterizing

1. Introduction

Polycrystalline aggregates are comprised of three mi-crostructural features: grain centers, grain boundaries, andregions affected by grain boundaries. In advanced highstrength steels, this picture is further complicated by thepossibility of grains being of different phases, and of grainsof the same phases but with different degrees of lattice im-perfection, i.e., dislocation density or sub-grain boundarydensity. It is these features that determine the mechanicalproperties, and any advanced understanding of microstruc-ture–property relationships requires a quantitative descrip-tion of these microstructural features.

Traditionally, microstructures have been described by ap-proaches based on visualization, i.e., how grains appear inthe optical and scanning electron microscope (SEM). Thisapproach led to the well-known Dubé classification1,2) ofvarious types of ferrite grains, based solely on the appear-ance of these grains. While this classification system doespermit differentiation, at least in simple systems, it does notallow for quantification. Hence, the traditional approachdoes not lend itself to either developing or applying accu-rate, valid structure–property relationships, which would beapplicable to both simple and complex microstructures.

Recent research has shown that modern, microalloyedHSLA steel hot-band coils with yield strengths of 490 MPacontain mixtures of polygonal, non-polygonal, acicular and

bainitic ferrite, where the various degree of mixture de-pends on the thermomechanical processing route.3) Whilethese types of ferrite are somewhat discernable using opti-cal microscopy or SEM, it has not been possible to properlyand accurately quantify the relative amount of each type offerrite using standard image analysis systems.

The EBSD technique has been widely used in analyzingsteel microstructures. The typical applications of EBSD in-clude grain/sub-grain size measurements, texture analysis,boundary characteristics, grain orientation and phase iden-tification. All of these applications are based upon the crys-tallographic orientation map, crystallographic structure dif-ferences or lattice perfection differences. However, thecomplexities of steel microstructures greatly challenge thesuccess of these applications. For some complicated steelmicrostructures, e.g., mixtures of various types of ferrite inHSLA steels and the microstructure of multiphase steels,the phase or microconstituent identification and quantifica-tion is still a major problem confronting many metallur-gists. In some recent studies,4–9) the diffraction pattern qual-ity (PQ), also called image quality (IQ), analysis has beennoted and used to investigate recrystallization behavior andphase transformations. However, only limited studies havebeen conducted dealing with the effects of the experimentalfactors and the data processing algorithms on the accuracyof IQ analysis results. Although some valuable informationhas been gained and interesting results have been published,

ISIJ International, Vol. 45 (2005), No. 2, pp. 254–262

© 2005 ISIJ 254

Image Quality Analysis: A New Method of CharacterizingMicrostructures

Jinghui WU, Peter J. WRAY, Calixto I. GARCIA, Mingjian HUA and Anthony J. DEARDO

Basic Metals Processing Research Institute (BAMPRI), Materials Science and Engineering, 848 Benedum Hall, University ofPittsburgh, Pittsburgh, PA 15261 USA. E-mail: [email protected]

(Received on September 21, 2004; accepted in final form on November 27, 2004 )

Polycrystalline aggregates are comprised of three microstructural features: grain centers, grain bound-aries, and regions affected by grain boundaries. It is these features that determine the mechanical proper-ties, and any advanced understanding of microstructure–property relations requires their quantitative de-scription. Traditionally, descriptions of microstructures have been based on visualization, i.e., how grains ap-pear in the optical or scanning electron microscope (SEM). While this may lead to classification systemsthat permit differentiation, it does not allow for quantification, especially in complex microstructures, anddoes not lend itself to either developing or applying structure–property relationships. The goal of this paperis to present a new approach to the characterization of complex microstructures, especially those found inadvanced modern high strength steels. For such steels, the new approach employs the fact that differenttypes of ferrite formed at different transformation temperatures have different dislocation or sub-grainboundary densities. Hence, measuring the degree of lattice imperfection of the grain centers of the ferrite isone way of first identifying, then grouping, and finally quantifying, the different types or forms of ferrite.The index chosen in this study to distinguish the degree of lattice imperfection is the image quality (IQ).Finally, as part of the new approach a procedure has been developed to improve the accuracy of applying IQmeasurements.

KEY WORDS: image quality; diffraction pattern; EBSD; microstructure; ferrite; grain boundary; phase identifi-cation; lattice imperfection.

there are still large amounts of information being ignoredor neglected during the over-simplified data processing. Forexample, IQ values not corrected for the grain boundarycontribution will be misleading, possibly causing investiga-tors to incorrectly determine the level of the IQ or its vol-ume fraction.

In this paper we present a new approach to the character-ization of complex microstructures such as may be found inhigh strength steels. The new approach is based on the factthat the different types of ferrite produced at different tem-peratures during cooling, for example following hot rolling,will have different densities of dislocations or sub-grainboundaries. In other words, measurement of the lattice im-perfection at the grain centers is one way of first identify-ing, then grouping, and finally quantifying, the differenttypes of ferrite. The index chosen to distinguish the degreeof lattice imperfection is the image quality (IQ); a latticedistorted by crystalline defects such as dislocations andsub-grain boundaries will have a distorted Kikuchi Pattern,leading to a lower IQ value. It can also be noted that be-cause nanohardness increases with lattice imperfection, weexpect there to be an inverse relationship between IQ andmicro- and/or nanohardness.

This new approach of using IQ to characterize mi-crostructures is particularly interesting because it provides ameans of quantifying the amounts of various types of fer-rite in complex HSLA steel microstructures. Dependingupon the transformation behavior during cooling, it is notuncommon for the polygonal, non-polygonal, acicular andbainitic forms of ferrite to co-exist in one sample, especial-ly in HSLA steels with yield strengths over 350 MPa. Thesedifferent types of ferrite are often readily discernable in op-tical microscopy and SEM. However, measuring their vol-ume fractions is not easily accomplished using conventionalquantitative metallographic techniques. Since both thetypes of ferrite and the IQ level of the individual ferritegrains vary with dislocation density, the measurement ofthe spectrum of IQ values in a given samples is necessary.

To take full advantage of the new IQ approach, the ef-fects of image processing on the IQ values are minimizedthrough a normalization procedure; a model considering theeffects of grain boundaries on the IQ distribution curve isdeveloped; and a multi-peak model for analyzing multi-component microstructures is introduced. These techniquesand some example applications will be discussed in thispaper.

The analysis of the IQ spectrum is a powerful and uniqueway of quantifying complex microstructures in HSLAsteels, and is critically important to understanding the ori-gins of strength in HSLA steels.

2. Experimental Procedure

2.1. Materials and Processing

Three steels were examined in this study and their chem-ical composition is shown in Table 1. Steel A is an IF steel.Starting with commercial hot band, Steel A was cold rolledto a reduction of 50%. Afterwards, two samples were nor-malized by reheating to and holding at 950°C for 30 and90 s, respectively, followed by air cooling to room tempera-ture. The resulting microstructures are designated A0 and

A1, and although both have a homogeneous, polygonal fer-rite structure, the grain size is different. With the A1 condi-tion, two different amounts of cold deformation, 15% and25%, were applied to develop microstructures A2 and A3,respectively. A2 and A3 are basically polygonal ferrite aswell, but having been deformed, they contain different dis-location densities.

Steel B is a dual phase steel. Starting with commercialhot band, Steel B was reheated and held at 806°C for 90 sand water quenched to develop a martensite�polygonalferrite microstructure, which is identified as B0.

Steel C is HSLA steel. Laboratory simulated hot-rollingwas carried out with the commercial as-cast slab material.The samples were reheated to 1 200°C for 30 min, aircooled to 1 150°C and rolled in three passes of 50% each,and finished at 900°C. From 900°C, the sample was water-spray cooled to 550°C and then furnace cooled to roomtemperature for simulation of the coiling stage of the stripprocessing. The microstructure C0, developed from thisprocedure, is a typical HSLA steel hot band microstructurewith various types of ferrite and some carbon rich micro-constituents.3)

A summary of the microstructures developed is given inTable 2.

2.2. Orientation Imaging Microscopy (OIM) Parame-ters

The EBSD data were collected using the TSL OIM DataCollection Software with the Philips XL-30 FEG SEM sys-tem. The standard operational procedure of EBSD observa-tion was employed, with 15 kV voltage and a spot size of 4(corresponding to a beam diameter in the order of 50 nm)were applied. The frame number was always set at 8 andthe hexagonal scan mode was used with step sizes of 2 µmand 0.5 µm. The choice of step size used in data collectioncan also affect the accuracy of the results, especially whenthe grain and sub-grain boundaries are of interest. It hasbeen shown that to obtain an accuracy of 10%, at least 5pixels per grain are required, and for an accuracy of 5%, aminimum of approximately 8 pixels per grain are re-quired.10) Since the grain sizes of the microstructures ofSteels A and B have been estimated to be larger than

ISIJ International, Vol. 45 (2005), No. 2

255 © 2005 ISIJ

Table 1. Chemical compositions of materials used in the study.

Table 2. Description of the microstructures under study.

20 µm, the step size of 2 µm was acceptable for these twoseries. The step size of 0.5 µm was employed for C0 mi-crostructure, which has a finer grain size. By choosing dif-ferent step sizes for different grain sizes, the error is re-duced significantly.

2.3. Data Analysis

The general analysis of the EBSD data was conducted byusing the commercial TSL OIM Analysis software. A grayscale image-quality map of a polygonal ferrite microstruc-ture developed by using this software is illustrated in Fig. 1.As clearly seen in this figure, the lower image quality is inthe darker areas adjacent to the grain boundaries. In thesegrain boundary regions (GBR) the low image quality is dueto the interference of diffraction patterns from two grainshaving different orientations. This explanation has beenwidely accepted. However, the effects of the GBR on theanalysis of the IQ spectrum have not been previously con-sidered and their importance has been neglected.

To better interpret an IQ spectrum, a newly developedprocedure will address three main tasks: (i) the contributionof the GBR, (ii) the normalization of IQ values and (iii) thestatistical distribution.

2.3.1. Accounting for the Grain Boundary Region (GBR)Contribution

In this work, the grain boundary is defined by the misori-entation of the two neighboring scanning points. In thecommercial TSL software, the statistical information ongrain boundary misorientation can be calculated; however,there is no means of identifying or studying the propertiesof any special grain boundaries of interest. In other words,it is impossible to filter out the GBR pixels. To mark out theGBR scan points, a technique was developed. In this tech-

nique, the misorientation between every two neighboringscan points is calculated based on its definition as describedby Randle.11) The grain boundary is defined as any interfacewith a misorientation larger than 15 deg. The GBR coversthe scan points that are contiguous with the grain bound-aries, as illustrated in Fig. 2. After examining all the scanpoints, the GBR points can be marked for further analysis.

2.3.2. Normalizing IQ Values

The most important hurdle to the application of imagequality in phase identification is the fact that the IQ value isvery sensitive to many operating factors, including theimage processing conditions. These factors may confusethe analysis of the information from the microstructure it-self and significantly reduce the ability to compare differentsets of IQ values. Normalization of IQ values is a way tominimize these misleading effects.

Assuming that the operating factors have an equal effecton each constituent in the microstructure, it is the absoluteimage quality values, not the relative image quality valuesfrom constituent to constituent, that are really influenced bythe operating conditions. The normalized IQ value can becalculated as.

...................(1)

where IQInitial is the absolute IQ value gained directly fromexperiment; and IQStandard is the average IQ value of a stan-dard sample, which is presumed to have a perfect crystalstructure and to therefore give the highest IQ value underthe same EBSD processing conditions.

Equation (1) can be used to normalize various kinds ofmicrostructures with extremely different components, be-cause they are normalized according to an independentIQStandard. To compare microstructures with the same com-ponents but different volume fractions thereof, a simplifiedequation, Eq. (2), can be used. The us of Eq. (2) does notneed the information of the standard specimen and there-fore saves time in scanning.

..............(2)IQIQ IQ

IQ IQNormalizedInitial Min

Max Min

��

��100

IQIQ

IQNormalizedInitial

Standard

� �100


© 2005 ISIJ 256

Fig. 1. A gray scale image-quality map of the fully recrystallizedpolygonal ferrite microstructure.

Fig. 2. Schematic of the Grain Boundary Region (GBR). Eachcell represents a scan point; the heavy black lines are thegrain boundaries and the adjacent cells in dark gray areGBR points.

where IQMax and IQMin are, respectively, the maximum andthe minimum IQ values in the scanning set.

In this study, the comparison of Steel A microstructureshas been conducted using Eq. (2) for normalization.

2.3.3. Normal Distribution and Multi-peak Model

Typically, the profile of the IQ distribution is bell-shaped,12) our results will confirm later. If we assume a nor-mal distribution, then for a single microconstituent a meanand standard deviation can be assigned to the IQ distribu-tion of a single symmetric peak of the distribution curve.Whenever an asymmetric shaped distribution curve is ob-served, it implies that more than one microconstituent ofdifferent mean IQ values exists. The multi-peak model isdeveloped on this assumption and used to divide the wholedistribution curve into several normal distributions, each ofwhich represents a certain microconstituent.

The multi-peak model can be described as:

..................................(3)

........................(4)

Min (k) .....................................(5)

....................(6)

where N is the number of the total scan points in the file; kis the number of normal distributions in the simulation; e isthe minimum acceptable error; ND(ni, m i, si) is the ith nor-mal distribution with the total data number of ni, the groupmean value of m i and the standard deviation of si.Adjusting every normal distribution and satisfying the Eqs.(3)–(5) produces a minimum difference between the initialIQ distribution and the sum of all single normal distribu-tions, as shown in the Equation (6). In this procedure eachof these normal distributions represents one component inthe microstructure.

2.4. Metallurgical Interpretation of Image Quality

The IQ is proportional to the sharpness of the KikuchiPattern, which is related to the presence of crystalline de-fects. An elastically distorted lattice will have a smearedKikuchi Pattern and a low IQ. A highly dislocated latticewill be expected to have a low IQ and a high nanohardness.To test this hypothesis, nanohardness measurements weretaken on pre-selected areas of known IQ using theNanoindentor IITM equipment at the Oak Ridge NationalLaboratory (ORNL). In addition, microhardness measure-ments were conducted in the grain centers of different IQvalues.

3. Results and Discussion

3.1. Grain Boundary Region

The gray scale IQ maps of microstructures A0 and A1are shown in Fig. 3. It is not surprising to find that the dark-er areas, which indicate lower image quality, are especially

located in the vicinities of grain boundary. When the GBRis included, as shown in Fig. 4(a), for both A0 and A1 mi-crostructures, there are two distinguishable peaks in the IQdistribution curve. Since there is only one simple microcon-stituent in both microstructures, the lower IQ peak couldonly correspond to the GBR points. With a smaller grainsize, A0, there is a higher area fraction of grain boundary

IQ� ��

ND ni i i

i

k

( , , )µ σ ε1

∑

IQ � ND ni i i

i

k

( , , )µ σ�1∑

N ni

i

k

��1∑


257 © 2005 ISIJ

Fig. 3. Gray scale IQ maps of (a) A0 and (b) A1 microstructures.

Fig. 4. IQ distribution curves of A0 and A1 microstructures. (a)Normalized IQ data including that of the GBR; (b) nor-malized IQ data without the GBR contribution.

per unit volume than with the larger grain size of A1. Thishigher fraction of grain boundaries implies a larger low-IQpeak, which is in fact observed in the IQ distribution curveof A0. After filtering out the GBR points with the algorithmmentioned earlier, two symmetric IQ distribution curves forboth microstructures are achieved, as shown in Fig. 4(b).This confirms that the lower IQ GBR points are the onlysource of the peak observed in the low IQ range and for amicrostructure with a single microconstituent, the IQ distri-bution is symmetric.

The GBR could also affect the mean IQ value of eachpeak in the distribution curve. As seen in Eq. (2), IQMin andIQMax are normalized to be 0 and 100, respectively. For acertain absolute IQ distribution, IQMin and IQMax valueswould affect the mean IQ value of the peaks after normal-ization. For the A0 and A1 microstructures, the IQMin valuemost likely comes from the low IQ GRB peak. For un-known reasons, the IQ values of the GBR usually have alarge variation. This makes the normalized IQ distributionless predicable. Under the effects of the GBR, the peak po-sitions of the two distributions, Fig. 4(a), are not wellmatched. Whereas with GBR filtered out, Fig. 4(b), themean IQ values are approximately the same.

3.2. Strain Distribution

There are two main purposes of this part of study. One isto confirm that the image quality is directly related to thelattice imperfection (dislocation density and/or defect den-sity); the other one is to quantify the retained cold strainusing IQ analysis, based on the symmetric distribution ofthe single phase and multi-peak model.

The image quality maps of microstructures A2 and A3strained 15 and 25%. respectively, are shown in Fig. 5. Theareas in gray have the normalized IQ values lower than 60,and the areas in white are those with higher normalized IQvalues. From a comparison of the IQ maps of Fig. 5 withthe misorientation maps of the same regions shown in Fig.6, it is evident that the low IQ areas are directly associatedwith low angle grain boundaries. Developed from the same

initial microstructure, the difference of the low-angle mis-orientation distribution in these two microstructures couldonly arise from the different amounts of applied cold defor-mation. An apparent correlation between the high disloca-tion densities introduced by cold deformation and the lowimage quality is evident. Based on this finding, the distribu-tion of the retained strain can be examined. As shown inFig. 5, the strain distribution indicated by the low IQ areasis inhomogeneous. When the strain is relatively small, Fig.5(a), there are very few low-angle misorientation bound-aries found inside the grains. This suggests that the appliedstrain was accommodated by grain boundary sliding or byreverse shear inside grains. With increasing strain, as inFig. 5(b), the low IQ area extended into some grain centers.The selection of grains where strain is retained, which is di-rectly related to the yielding behavior of steels, might bedetermined by the orientation of the grains. A more de-tailed treatment of this topic deserves to be undertakenelsewhere.

The multi-peak model has been used to analyze the colddeformed A2 and A3 microstructures, and the results areshown in Fig. 7. The initial EBSD data have been normal-ized and the grain boundary contributions have been fil-tered. For both microstructures, the IQ distribution curvecould be best fitted with two normal distribution curves.The low IQ peak corresponds to the areas with retainedstrain; while the high IQ peak represents those areas thatare affected very little by the deformation. The fraction ofthe data points of each peak is given in Table 3. It is some-what surprisingly that the fraction of the low IQ peak nu-merically matches very well with the strain applied.However, this result does appear to support the idea that theIQ analysis and the multi-peak model can be used to identi-fy the strain-retained area and quantify the amount of colddeformation. Further work is in progress to decide whetherthis good agreement is real or fortuitous.


© 2005 ISIJ 258

Fig. 5. IQ maps of (a) A2 microstructure and (b) A3 microstructure. Areas in gray have normalized IQ value lower than60 and the areas in white have IQ values higher than 60.

3.3. Measurement of the Volume Fraction of Marten-site

As one simple application of IQ analysis, the martensitevolume fraction has been measured for the dual phase steelwith the a�a� microstructure, B0, and the results areshown in Figs. 8 to 10.

An optical micrograph of the B0 structure in Fig. 8shows the mixture of polygonal ferrite and martensite. Asexpected from the difference in dislocation density of thesetwo phases, two distinct image quality regions are observedin the IQ map of Fig. 9, with polygonal ferrite being thelight area with high IQ and martensite being the dark area

with low IQ.Further analysis of the normalized IQ data was conduct-

ed using the multi-peak model. As shown in Fig. 10, twonormal distribution peaks have been constructed and theirsum perfectly matches with the distribution curve of the ex-perimental data. Assuming all data points in the low imagequality peak come from martensite, the fraction of the lowIQ peak gives the volume fraction of the martensite phasein this mixed-phase microstructure, which is found to be43.9%. Compared to the manual measurement from optical


259 © 2005 ISIJ

Fig. 6. EBSD misorientation maps of (a), A2 microstructure, and (b), A3 microstructure. The black thick lines are grainboundaries with misorientations larger than 15 deg; and the gray thin lines are those misorientation from 2 to15 deg.

Fig. 7. The IQ distribution analysis of (a): A2 microstructureand (b): A3 microstructures using the multi-peak model.

Fig. 8. Optical micrograph of the B0 microstructure etched with2% Nital. The black area is martensite.

Table 3. The multi-peak model analysis results of the coldrolled A1 microstructure.

microscopy, which is approximately 40%, the martensitevolume fraction estimated using IQ analysis is reasonablyaccurate.

The major advantage of adopting the multi-peak modelfor IQ analysis is that it can be applied to various kinds ofasymmetric IQ distribution curve. IQ analysis has beenused in the past to measure martensite volume fractions ina�a� microstructures only in the case of double peaks de-tected.6) In the current work, the new multi-peak modeldoes not require any threshold to be pre-selected.Furthermore, the capability of this technique has been en-hanced by considering the grain boundary influence anddealing with the condition when no trough exists in the dis-

tribution curve.The accuracy of the IQ analysis can be affected when

areas of unexpected high dislocation density areas are de-veloped. For the B0 microstructure, during the martensitictransformation upon quenching, a significant number ofdislocations are produced in the ferrite grains surroundingthe martensite. The image quality of these ferrite areas ismuch reduced and they might be mistaken for martensite.This explains why the measurement using the IQ techniqueis slightly higher than the one gained from traditional opti-cal or SEM methods. An effort to minimize and correct thiserror is ongoing.

3.4. Analysis of a Multi-phase (Mixture of Types ofFerrite) Microstructure

IQ analysis and the multi-peak model have been adoptedto analyze the C0 microstructure, a mixture of several dif-ferent austenite decomposition products as the optical mi-crograph shows in Fig. 11. The corresponding IQ analysisresults are presented in Figs. 12 and 13. Because variouscooling rates were used in the run-out-table simulation, i.e.,air cooling and water spray cooling, followed by coiling,taken together with the complex CCT diagram expected forthis steel, several types of ferrite were formed at differenttemperatures during cooling. For a complex microstructurelike C0, the visual identification of the ferrite types basedon the morphological differences is extremely difficult, ifnot impossible. It is therefore nearly impossible to get anaccurate measure of the volume fraction of the austenite de-composition products by using any of the traditional mi-crostructural analyses.

EBSD maps of the C0 microstructure are shown in Fig.12, in which (a) is the grain boundary and subgrain bound-ary map and (b) is the gray scale image quality map. It isobserved that the grains with interior subgrain boundariesalways have relatively lower IQ values compared to thosewithout subgrains. The ferrite types formed at low tempera-tures, e.g., acicular and bainitic, have a higher degree of lat-tice distortion and a larger density of subgrain boundaries.Even for the grains without subgrain boundaries, the IQvalues vary noticeably from one grain to another. This vari-ation of IQ values implies a difference in lattice imperfec-tion, e.g., dislocation density, which is very much deter-mined by the mechanism and temperature of formation ofthe ferrite type. Consequently, the differentiation of IQ val-


© 2005 ISIJ 260

Fig. 9. An IQ map of the B0 microstructure. White areas havenormalized IQ values larger than 60; light gray areashave normalized IQ values between 40 and 60; and thedark gray areas have the values lower than 40.

Fig. 10. The IQ analysis of the same B0 microstructure as shownin Fig. 9 using the multi-peak model.

Fig. 11. An optical micrograph of C0 microstructure etched with2% Nital.

ues caused by the lattice imperfection serves to identify thedifferent types of ferrite.

The result of applying the multi-peak model to the C0microstructure is shown in Fig. 13. A total of five normaldistribution peaks are summed up and the difference fromthe experimental data curve is around 1.7%. Because thetraditional classification of ferrite is mainly based on the vi-sualized grain morphology and the IQ method relies on thelattice imperfection, the correspondence of the multi-peaksin Fig. 13 to the particular ferrite types can only be as-sumed at present. The first assumption is that each compo-nent of the final microstructure has its own, individual char-acteristic peak in the IQ distribution curve. The second as-sumption is that the mean value for each IQ peak decreaseswith the falling transformation temperature of each type

ferrite. Thus, some correspondence of the microstructuralvariation and the IQ distribution curve may be possible, asindicated in Fig. 13.

The IQ analysis itself is a new method of characterizingmicrostructures since it is based on a 3-D view of latticeimperfection, as opposed to the traditional analysis basedon 2-D surface visualization. Furthermore, the potential ap-plication of this technique has been greatly expanded sincethe IQ value shown in Fig. 14 to be inversely proportionalto the mechanical properties as represented by the micro-hardness or nanohardness. This result is not surprising,given that the lattice defects influence both the IQ and me-chanical properties, but it does raise the promise that the IQapproach will become a valuable tool in developing mi-crostructure-mechanical property models of complex mod-ern high strength steels.

4. Conclusions

To characterize complex multi-component microstruc-tures in a more effective and detailed fashion, a new proce-dure using EBSD diffraction pattern image quality has beendeveloped. In this procedure, the variation of IQ valuescaused by image processing has been minimized through anormalization procedure. Also, for the first time, the influ-ence of the GBR on the image quality distribution is ac-counted for when characterizing microstructures. Finally, amulti-peak model simplifies the task of separating the IQspectrum into component peaks and thereby strongly en-hances the capability of the IQ procedure to analyze morecomplicated microstructures.

Using this innovative IQ procedure, dislocation densitygradients in cold deformed samples have been successfullyobserved. This development, combined with the propertreatment of the grain boundary effects, may be used tostudy the recrystallization behavior with improved accura-cy. There is also the expectation that when combined withthe EBSD orientation map, the IQ procedure will become auseful tool for studying fundamental deformation mecha-nisms.

In another illustrative application of this technique, themartensite volume fraction was evaluated in a martensite�ferrite microstructure. However, the observation of high


261 © 2005 ISIJ

Fig. 12. EBSD maps of the C0 microstructure. (a) The grainboundary and misorientation map, where the thickcurves are for grain boundaries with misorientationslarger than 15° and the thin curves are for those from 2°to 15°. (b) The gray scale image quality map of theidentical area of (a).

Fig. 13. The IQ distribution analysis of the C0 microstructureshown in Fig. 12 using the multi-peak model.

Fig. 14. Correlation of the microhardness and nanohardnessmeasurements with image quality measured at the samelocation

dislocation density areas in the ferrite grains adjacent to themartensite is a previously unacknowledged source of error,even though it sheds light on the inhomogeneous nature ofthe retained strain.

Finally, this new IQ procedure may be used to analyzeand quantify the complex microstructures of HSLA steelswith yield strengths over 350 MPa and which contain differ-ent types of ferrite. This application may be especially valu-able for relating microstructure to mechanical properties, assuggested by the demonstration that the IQ values are in-versely related to the nano- and micro-hardness.

Acknowledgements

The authors would like to thank Dr. Andrei Rar for histechnical assistance with the nanohardness measurement.Research through the Oak Ridge National laboratorySHaRE User program was sponsored by the Division ofMaterials Science and Engineering, U.S. Department ofEnergy, under contract DE-AC05-00OR22725 with UT-Battelle, LLC.

REFERENCES

1) C. A. Dubé: PhD Thesis, Carnegie Institute of Technology, (1948).2) H. I. Aaronson: Decomposition of Austenite by Diffusional

Processes, Interscience, New York, (1962), 387.3) J. E. Garcia, J. Wu, P. Wray, C. I. Garcia and A. J. DeArdo: 44th

Mechanical Working and Steel Processing Conf, ISS, Warrendale,PA, (2002), 965.

4) M. P. Black and R. J. Higginson: Scr. Mater., 41 (1999), No. 2, 125.5) A. W. Wilson and G. Spanos: Mater. Charact., 46 (2001), 407.6) A. W. Wilson, J. D. Madison and G. Spanos: Scr. Mater., 45 (2001),

1335.7) B.-Y. Jeong, R. Gauvin and S. Yue: Microsc. Microanal., 8 (2002),

700.8) A. D. Rollett, M. L. Taheri and B. S. El-Dasher: Plasticity ’02, the

9th Int. Symp. on Plasticity and Its Current Applications, NEATPress, Fulton, MD, (2002), 42.

9) B.-Y. Jeong, R. Gauvin and S. Yue: 44th Mechanical Working andSteel Processing Conf., ISS, Warrendale, PA, (2002), 173.

10) F. J. Humphreys: Jo. Mater. Sci., 36 (2001), 3833.11) V. Randle: The Measurement of Grain Boundary Geometry, Institute

of Physics Publishing, Bristol and Philadelphia, (1993), 17.12) X. Tao and A. Eades: Microsc. Microanal., 8 (2002), No. 2, 692.


© 2005 ISIJ 262

image quality analysis: a new method of characterizing

Documents