12 automatic feature extraction from micrographs of forged superalloys تحلیل ساختار 718...

8/13/2019 12 Automatic Feature extraction from micrographs of Forged superalloys 718 .pdf

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Overview High-Temperature Alloys

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describe the overallsignicance of this paper?

This paper describes the methodand procedure for fully automaticanalysis of light micrographs of thenickel-based superalloys Inconel718 and Allvac 718Plus.Grain size estimation as wellas phase evaluation is possiblewithout human interaction.Scratches and twins are removedautomatically to show the rightgrain boundaries for evaluation.

describe this work to amaterials science and engineeringprofessional with no experience

in your technical specialty?The main advantage of thisinvestigation is the possibilityof evaluating the microstructureof nickel-based superalloys withimage processing methods withoutany human interaction. Thecorrelation between verication ofmanual microstructure evaluationwith planimetric method and themethod presented in this paper isvery high.

describe this work to alayperson?

This paper describes the method for fully automatic evaluation ofimages that show different featuressuch as grains, boundariesbetween grains, or special

particles. The major goal of thisimage analysis is to segment andextract those features and denethem without human interaction.The results of the image analysis

provide information about thenumber of grains, the average grainsize, the grain size distribution,the number of particles, and their

fraction within the picture.

The manual determination of metal-lurgical parameters of forged superal-loys can be dramatically improved byautomatic, image-processing-based

feature extraction. With the proposedmethods, the typical errors during grainsize estimation for Inconel 718 and All-vac 718Plus, caused by twins and

other artifacts like scratches, can beeliminated. Different processing strate-gies for grain size estimation allow theapplication of a wide range of ASTMgrain size numbers from G3 to G12with the typical variations in the mani-

festation of metallurgical details andthe magnication-related limitations ofimage quality. Intercept counting strat-egies show advantages for samples with

pronounced anisotropy and can pro-duce detailed statistics on grain orien-tation. In addition to a single grain sizenumber, grain size histograms offer amore precise description of the materi-al properties.

INTRODUCTION

For quality management of enginecomponents or structural parts in air-craft, the evaluation and analysis of mi-crostructure is essential to estimate me-chanical properties. In particular, thegrain size, the different precipitates,and their distances to each other decidewhether a forged part passes customerrequirements or not. Developing newforging processes, testing new materi-als, or prototyping always requires im-age analysis to evaluate the material be-havior. Commercial semi-automaticmethods of analyzing micrographsbased on stereometric aspects showquite good results depending on thequality of sample preparation and thecorrect interpretation of the results.Nevertheless, scratches or twin bound-aries are not removed automatically

Automatic Feature Extraction fromMicrographs of Forged Superalloys

E. Berhuber, A. Rinnhofer, M. Stockinger, W. Benesova, and G. Jakob

formed by a skilled materials engineerare necessary but very time consuming.A new, fully automatic technique forprocessing light microscopy imageswas initiated by the need for fast andcertain evaluation of micrographs,without human intervention and specif-ic sample preparation.

Nickel-based superalloys like Inco-nel 718 are commonly used in aircraftto design low- and high-pressure tur-bine discs, shafts and engine mounts,and other structural parts. Although theforging process for superalloy parts iswell known, monitored, and super-vised, the analysis of microstructuredevelopment during and after the ther-momechanical treatment gives quickinsight into process quality as well asexpected mechanical properties. Be-cause of its high-temperature strength,good weldability, and corrosion resis-tance, Inconel 718 is one of the mostinvestigated superalloys. Therefore, therelationship between microstructuralfeatures and mechanical properties iswell known. The newly developed All-vac 718Plus superalloy was designedfor Waspalloy-like working tempera-tures without losing the good process-ability of Inconel 718. However, not allrelations between microstructure andmechanical properties are completelyclear for this superalloy and thereforesome further investigation is needed.1 The microstructure of both alloysconsists of a nickel-rich matrix, whichhas a face-centered cubic (fcc) latticestructure with many different precipi-tates. In both alloys a small fraction ofisolated, big blocky primary carbidesand carbonitrides that also contain cer-tain amounts of boron can be found.Due to their negligible inuence onmechanical properties a quantitativeanalysis can be omitted. The nanocrys-

and weak grain boundaries are often ig-nored. Therefore, the average grain sizeresults can be deceptive. For most ac-curate results, manual methods per-


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talline precipitation-hardening phasesin both alloys are of Ni3X type and ei-ther coherent or semi-coherent with thematrix. In Inconel 718 the phase is dueto the high-niobium-content mainlymetastable (Ni3Nb). Depending onthe exact chemical composition andthe thermomechanical history, a cer-tain fraction of stable (Ni3Al,Ti) pre-

cipitation could be observed.2-6

Above650C, due to the high surface energyof the semi-coherent phase, particlesgrow with time and nally transform toa stable phase, which leads to a sig-nicant reduction of high-temperaturestrength and creep resistance.3. Due tothis, levels of aluminum, titanium, andniobium have been carefully balancedin the composition of Allvac 718Plus tostabilize the coherent phase insteadof .7 The size of and particles is

generally small, typically less the 200. Thus, a magnication of 30,000and, therefore, the use of high-resolu-tion secondary or transmission electronmicroscopes is necessary for analysis.8

These particles can not be consid-ered in the automatic image analysis oflight microscopy. In fact, the amountof the coarser, stable Ni3Nb phase,which can be observed in both alloys,gives indirect information on the pos-sible fraction but not size of the neparticles. The phase appears in glob-ular or acicular morphology dependingon thermomechanical processing andheat treatment and precipitates prefer-entially on grain and twin boundaries.In Inconel 718 the particles dissolvecompletely at a composition-depen-dent temperature of ~1,030C, whereasin Allvac 718Plus this temperature isclose to 1,000C (Figure 1).

Due to the size of the-phase par-ticles, which are typically coarser than1 m, it is possible to quantitativelyanalyze the amount and morphologyusing conventional light microscopy.Besides precipitates, the major effecton mechanical properties comes fromthe grains, their size, and their distri-bution. Depending on the parame-ters (temperatures, strains, and strainrates) of the thermomechanical treat-ment as well as the initial microstruc-ture, a ne uniform grain structure or acoarser grain structure can be obtained.For a ne grain size up to ASTM 12,which is requested in most engine disc



Figure 6. A histogram view of planimetric grain size evaluation before and after twin removal. The small images on top are just subframes of the evaluated micrograph images.

Figure 5. The results of the feature extraction task. Left graph: a free intercept length histogram based on circular scan line evaluation. Center

graph: anisotropy factor computed from straight scan lines in various directions. Right graph: grain size number histogram based on theplanimetric evaluations. Top table: grain size numbers from intercept count evaluationall results before and after twin correction.

Figure 4. Allvac 718Plus with globular (green objects) andneedle-shaped (yellow objects) delta phase and carbide inclu-sions (red objects).

50 m


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Table I. Comparison of Planimetric Grain Size Evaluation before and after Twin Removal

Manually ManuallyGraded by Graded byComparison Method Intercept Count x y Error x y Error

G3 3.10 0.25 5.44 5.41 2.09 4.19 4.02 0.54G4 3.29 0.18 6.03 5.98 2.11 5.0 4.98 0.80 3.14 0.09 4.69 4.68 1.40 3.12 3.34 0.03G5 5.69 0.07 6.94 6.92 1.14 6.04 6.0 0.14 5.69 0.07 7.29 7.13 0.80 6.36 6.35 0.06G6 6.30 0.07 7.19 7.31 0.59 6.16 6.25 0.64G9 9.77 0.07 11.11 11.20 1.39 10.53 10.62 0.805 1.36 0.18 0.59 0.55

With Twins With Twin Elimination

applications, processing at tempera-tures below-phase dissolution is re-quired. Due to dynamic recrystalliza-tion, grain renement can be achievedduring forging operations. To prohibit

grain growth during reheating or so-lution treatment processes a sufcientamount of incoherent precipitates isnecessary to pin the grain boundaries.The ne grain size in general leads toan increase in yield and ultimate tensilestrength as well as better fatigue stressrupture properties also at elevated tem-peratures. Unlike disc applications forforged blades in stationary gas turbinesas well as aircraft engines, a coarsergrain structurearound ASTM 4isrequired by design engineers to guar-antee good creep properties. In ne-grained disc applications good creepproperties are also sometimes required.Thus, the amount of particles at thegrain boundaries must be minimized. While the development of automaticmicrograph evaluation for Inconel 718focused on the microstructure of forgedparts, Allvac 718Plus was analyzed inas-received, forged, solution-treated,and aged and over-aged condition. Thisalso requires an evaluation of whetherglobular-phase morphology or acicu-lar shape particles are dominant in themicrostructure.

IMAGE PROCESSINGSTRATEGIES

Sophisticated image processingstrategies need to be applied to extractthe requested features from micrographimages automatically. To evaluate grainsize according to ASTM standards,10-12 the identication of the grain boundaryis necessary. Since in micrograph im-

ages of these superalloys closed linestructures dening the grain boundarycompletely can seldom be found, solu-tions that can also compute the grainsize from incomplete boundary struc-

tures need to be applied. The generalworkow for the solution described inthis paper is demonstrated in Figure 3.The application of planimetric methodscan not be applied directly because ofthe gaps in grain boundaries. Alterna-tive methods for estimation suggestedby the ASTM standards are differenttypes of intercept-counting approaches.These methods, especially applied au-tomatically with a high number of scanlines, can produce reliable results evenfor images with boundaries that are dif-cult to reconstruct. Applying interceptcounting for scan lines at different ori-entations will also produce the basicdata for anisotropy measurement in thecutting plane of the sample. All intercept counting methods relyon the best possible separation betweenboundary segments and other objectsvisible in the image. The removal ofnon-boundary objects in the segmenta-tion image uses different a priori knowl-edge for multiple classication steps. Arst segmentation is performed using acombination of local and global thresh-olding. A color-feature- and texture-feature-based computation step is re-sponsible for detecting colored inclu-sions such as carbides and carbonitridesamong the objects. As a result of theetching process these particles are oftennot in the same plane as the grainboundaries and therefore are out of fo-cus during the capture of a sharp grainboundary image. This typical soft anduniform surface appearance is the best

feature for segmentation, since the col-or and shape allow no differentiationfrom the grains of the superalloys. Further classication of the objectsgenerated by the segmentation is main-ly based on shape and gray value histo-gram features for separating these objects in eight different categories. De-pending on the type of material, these

categories are combined in three class-es: a noise class, a-phase class, and aboundary segment class. More detailsof this iterative procedure of classica-tion steps followed by grayscale imagereconstruction13 steps can be found inReferences 14 and 15. Boundary segmentation unfortunate-ly will also recognize twins as grainboundary segments. Twins are oftenmore easy to detect than the real bound-aries by local operations due to their

high contrast and sharp line shape (Fig-ure 2). The presence of twins will mis-lead automatic grain size evaluationand give higher ASTM grain size num-bers if not treated correctly. The factthat twins are parallel line segmentswithin a certain distance, which willcompletely or partially overlap eachother, is the most important feature forthe discrimination between twins andgrain boundaries. A special form ofHough transform, developed for the ro-bust and fast detection of these parallelline segments, identies twins.16 Twinswill be marked to be erased from thebinary boundary image, which is usedfor both implemented types of grainsize evaluation methods. A gap, whichoften appears in the grain boundary be-tween two twin lines, can be closed au-tomatically by using the information oftwin line starting- and end-point posi-tions and a simple check, if two corre-sponding twin lines are connected by aboundary segment.

FEATURE EXTRACTION

The intercept method computes thegrain size directly from the mean inter-cept distance according to Equation 1where Pl [mm1] is the number of inter-ceptions per millimeter. Unfortunately,these methods only work for unimodalgrain size distributions. Bimodal ormultimodal size distributions are unde-tectable and thus there is no informa-tion about real grain size for bimodaldistribution in the intercept length



histograms. G = (6.643856 * log10(Pl)) 3.288 (1) In Allvac 718Plus samples in as re-ceived condition, distinctive bimodalgrain size distributions can be found.The grain size of these samples cannot be evaluated correctly with the in-tercept methodthe short intercept

length of the small-grain fractions cannot be distinguished from short chordsof the larger-grain fractions. A plani-metric evaluation method is necessaryto compute the grain size of each grain(Equation 2). In addition, for this mate-rial boundary segments are often miss-ing and thus additional processing stepsare necessary to compute closed grainboundaries. Starting with the binaryboundary image (containing the in-complete boundaries) a distance trans-

formation followed by a watershed17,18

algorithm has the ability to produceclosed grains. Although the producedgrain boundaries are sometimes not inthe same position where a human grad-er would set them, the statistical resultof the grain size distribution is precise,as shown in Equation 2:G = (3.321928 * log10(NA)) 2.954) (2)where NA [mm2] is the number ofgrains per mm2. Additional, highlyuseful information from this classica-tion is the identication of-phase par-ticles (Figure 5). With the knowledgeof the position of grain boundaries andthe position of-phase precipitates, theminimal distance between these twocan be computed and visualized as ahistogram. This evaluation works di-rectly for ASTM grain size numbers upto 10. For smaller grains, the separationbetween grain boundary and-phaseparticles is often not possible. Further-more, the grain boundaries can onlybe determined using the-phase pre-cipitates decorating the expected line-shaped grain boundaries; thus, the dis-tance between boundary and-phaseparticles decreases to zero. Only a fewprecipitates remain within the grains(see also Figure 5). In Allvac 718Plus there are typicallytwo different shapes of-phase precipi-tates that need to be evaluated separate-ly. The more globular-shaped phase,

which can be detected inside the grains,and the needle-shaped-phase parti-cles, which are preferentially found atthe grain boundaries, are shown in Fig-ure 5. The discrimination between thedifferent types is done by a statisticalclassication algorithm using shapefeatures and gray-value histogram fea-tures as an input.

C ONCLUSION

For Inconel 718 micrographs, thegrain size evaluation with both imple-mented intercept counting methodsshows a high accuracy. The vericationof manual microstructure evaluationwith planimetric methods and interceptcount methods show better correlationfor the latter (Table I). The result of alldifferent methods for grain size evalua-tion, together with meaningful results

from intermediate processing steps, canbe visualized in great detail, as shownin Figure 4. The strong bimodal sizedistribution of some regions of as-re-ceived Allvac 718Plus can be best de-scribed in histogram form (see Figure6). The changes caused by twin han-dling are clearly visible (Table I), themean errors of the grain size number(assuming the mean value of the inter-cept count evaluations as a basis) couldbe reduced from 1.36 to 0.18. Due tothe limited number of complete grainsper image, the histograms do not showa smooth distribution. An image acqui-sition method and adequate equipmentfor scanning bigger regions assisted byautomatic image stitching would be anoptimum improvement of the describedmethods. An automatic software tool which isable to perform microstructure imageanalysis with a minimum of deviationto manual methods has been developed.The highly requested improvement inquality assurance and research evalua-tions in combination with a high stabil-ity of the feature extraction process of-fers higher efciency in micrographanalysis and therefore early indicationsof mechanical properties. In recent andfuture developments, the automatic ex-traction of metallographic features fortitanium alloys and martensitic stain-less steel will be implemented in thesame program.

ACKNOWLEDGEMENTS

This work has been carried out in part within the K plus CompetenceCentre Advanced Computer Vision.This work was funded from the K plusProgram.

References

1. Wei-Di Cao,Superalloys 718,625,706 and VariousDerivates,ed. E.A. Loria (Warrendale, PA: TMS, 2005),pp.165177.2. R. Brgel,Handbuch Hochtemperatur-Werkstoff- technik, 2 (Auage: Pub. Vieweg, 2001).3. M. Stockinger, Mikrostrukturelle Simulation des Ge-senkschmiedens von Nickelbasis-Legierungen (Ph.D.thesis, University of Technology, Graz, 2003).4. M. Sundararaman, P. Mukhopadhyay, and S. Baner- jee, Met. Trans. A, 23 (1992), pp. 20152028.5. R. Cozar and A. Pineau,Met. Trans., 4 (1973), pp.4759.6. M. Sundararaman, P. Mukhopadhyay, and S. Baner- jee, Superalloys 718, 625, 706 and Various Deriva- tives , ed. E.A. Loria (Warrendale, PA: TMS, 1994), pp.

419439.7. Wei-Di Cao and R. Kennedy,Superalloys 2004,ed.K.A. Green et al. (Warrendale, PA: TMS, 2004), pp.91100.8. J.F. Radavich and T. Carneiro,Superalloys 718, 625,706 and Derivates , ed. E.A. Loria (Warrendale, PA:TMS, 2005), pp. 329340.9. Ch. Stotter,2nd Year Status Report No. 2 (CD-Lab.for Materials Modelling and Simulation, University ofLeoben, 2007).10. E-112 Standard Test Methods for Determining Av-erage Grain Size, ASTM Standards (West Con-shohocken, PA: ASTM International, 2004).11. E-1382 Standard Test Methods for DeterminingAverage Grain Size Using Semiautomatic and Auto-matic Image Analysis,ASTM Standards (West Con-shohocken, PA: ASTM International, 2004).12. B. Roebuck,Materials and Science Technology , 16(2000), pp. 11671174.13. L. Vincent,IEEE Transactions on Image Process- ing , 2 (1993), pp. 176201.14. W. Benesova, A. Rinnhofer, and G. Jakob,2006IEEE International Conference on Image ProcessingProceedings (Piscataway, NJ: IEEE, 2006), pp. 27492752.15. A. Rinnhofer et al.,2006 IEEE International Confer- ence on Image Processing Proceedings (Piscataway,NJ: IEEE, 2006), pp. 391394.16. G. Jakob et al.,Machine Vision Applications in In- dustrial Inspection XIV, vol. 6070, ed. Fabrice Meri-audeau and Kurt S. Niel (Bellingham, WA: SPIE, 2006),pp. 192202.17. J.B.T.M. Roerdink and A. Meijster,FUNDINF: Fun- damenta Informatica , 41(3) (2000), pp. 187228.18. L. Vincent and P. Soille,IEEE Transaction on Pat- tern Analysis and Machine Intelligence , 13 (1991), pp.583598.

E. Berhuber, materials and process engineer, andM. Stockinger, head of R&D, are with BoehlerSchmiedetechnik GmbH & CoKG; A. Rinnhofer,head of the industrial inspection workgroup, G.Jakob, software developer, and W. Benesova, re-searcher, are with Joanneum Research Forschun-gsgesellschaft mbH, Institute of Digital Image Pro-cessing. Mr. Rinnhofer can be reached at +43-316-876-1742; e-mail [email protected].

12 automatic feature extraction from micrographs of forged superalloys تحلیل ساختار 718...

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