Communication: Characterization of Spatial Distribution of Graphite Nodules in Cast Iron. Simon N. Lekakh. International Journal of Metalcasting

Transcription

1 Communication: Characterization of Spatial Distribution of Graphite Nodules in Cast Iron Simon N. Lekakh International Journal of Metalcasting ISSN Volume Number 4 Inter Metalcast (207) : DOI 0.007/s

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3 COMMUNICATION: CHARACTERIZATION OF SPATIAL DISTRIBUTION OF GRAPHITE NODULES IN CAST IRON Simon N. Lekakh Missouri University of Science and Technology, Rolla, MO, USA Copyright Ó 206 American Foundry Society DOI 0.007/s Abstract Important properties of cast iron, such as fatigue strength, wear resistance, and low-temperature toughness, relate to spatial distribution of graphite nodules. Characterization of spatial distribution can also provide insight into the solidification sequence in casting. An automated SEM/EDX analysis was utilized to distinguish graphite nodules from other structural features (pores and inclusions). The twodimensional near-neighbor distance (NND) between nodule centers was calculated for three equal sets of nodule diameters (small, medium, and large) in each cast iron. Comparison of measured spatial distributions and ideal random distribution was executed by plotting the mean and variance ratios of NND on a spatial distribution quadrant. This method was used to clarify clustering or ordering tendencies of graphite nodules in studied cast irons. The suggested procedure was used to verify the effects of inoculation and the cooling rate on spatial distribution of graphite nodules. Inoculation of sand casting increased nodule counts, decreased mean NND, and eliminated clustering of small graphite nodules precipitated at the solidification end. Intensive surface cooling of a continuously cast bar significantly increased nodule count near the external surface and decreased NND without changing spatial distribution. The suggested analysis can be used as a tool for cast iron quality control and process development. Keywords: cast iron, structure, graphite nodule, spatial distribution Dimensional Distribution of Graphite Nodules The morphology of individual graphite nodules and its spatial distribution in cast iron are both important structural parameters in judging the casting properties. The graphite s shape, size, and quantity are determined by applying different algorithms and rules for digital images of the structure. 3 These methods are mainly used for quantitative representation of the graphite phase morphology and for the qualitative classification of a cast iron structure. Digital optical metallography is a commonly used method for the determination of cast iron structures; however, this method presents several problems, two of which are important for cast iron characterization:. Distinguishing microstructure features by optical contrast, for example, large graphite particles from micropores or small graphite particles from nonmetallic inclusions. To partially solve this problem, a2-to5-lm threshold is used to cut off the possible effect of inclusions and artifacts related to limiting optical resolution. Application of an automated scanning electron microscopy/energy dispersive X-ray (SEM/EDX) method for analysis of cast iron structure resolves these limitations. 4 7 An automated SEM/EDX method has high resolution, and chemistry of features can be determined for its classification. Optimized settings of an automated SEM/EDX analysis for specific applications were discussed. 8 Figure illustrates the possibility of distinguishing nonmetallic inclusions from small graphite nodules using this method. 7 A total of 2000 features were counted for the specimen, and a search area was divided into 4 electronic fields with high precision of field stitching. 2. Any analysis (optical or SEM) from a random polished section provides only two-dimensional structure characterization of the true three-dimensional geometry. The counted small graphite circles in the two-dimensional analysis present a International Journal of Metalcasting/Volume, Issue 4,

4 Frequency, /mm sum of small spherical nodules and the cuts of larger spheres. Therefore, the two-dimensional data need to be converted into the real volume distribution of graphite nodules. Fortunately, for spherical particles, it is possible to computationally restore true three-dimensional graphite nodule (spheres) size distributions from twodimensional measurements of graphite sections (circles) by applying different computational algorithms. 5,9,0 The obtained three-dimensional distributions of graphite nodules showed a possibility of bimodal diameter distributions. 7,0, However, it is not possible to restore the real three-dimensional (X, Y, Z) spatial distribution from two-dimensional (X, Y) graphite nodule coordinates obtained from a random section. Spatial Distribution of Graphite Nodules Inclusions Graphite Total D nodule diameter, µm Figure. Two-dimensional diameter distributions of nonmetallic inclusions and graphite nodules obtained from automated SEM/EDX analysis. 7 Important properties of cast iron, such as fatigue strength, wear resistance, and low-temperature toughness, relate to the spatial distribution of graphite nodules. Characterization of the graphite nodule spatial distribution can also provide insight into casting solidification. 2 For example, fractal analysis was used for the determination of lacunarity a measure of non-uniformity filled space by nodules which was affected by prime austenite and graphite solidification modes. The methods of evaluation of spatial distributions of secondary phases have been developing during the last half century since the first digital microscopes became available. 3 6 The Voronoi tessellation method is used to visualize a spatial distribution of graphite nodules. 5 The Voronoi tessellation divides a n-dimensional space into convex n-dimensional Voronoi polytopes that fill space without overlap. According to the definition of Voronoi tessellation, a Voronoi cell associated with a nucleus P in space contains all points in that space that are closer to P than any other nucleus. These methods provide information about a specific space associated with each particle, and they are widely used in modeling different structures. However, the tessellation methods are seldom applied for the analysis of graphite nodules 2 because of its relative complexity and difficulties with interpretation of the result in everyday foundry practice. In this study, the center of each nodule was defined in an automated SEM/EDX analysis of polished section using an 8-sword raster at high magnification, and the center-tocenter near-neighbor distance (NND C ) was calculated without considering nodule diameters. Typically, 2000 nodules were counted in each sample using several stitched electronic fields. Obtained from an automatic SEM/ EDX analysis, clean graphite nodule data were digitized and ImageJ 2 software was used to build Voronoi tessellations. 7 A practical method to interpret a spatial distribution of graphite nodules in castings is suggested. In each case, graphite nodules were sorted by diameter and after that were divided into three equal by nodule number classes: small, medium, and large. A set of NND C was calculated for each class. It was done to assess the solidification sequence assuming that early nucleated nodules would be larger in diameter than nodules nucleated at the solidification end. The experimental NND C distributions of graphite nodules were compared to the near-neighbor distance for virtual randomly distributed points using methodology. 5 For randomly generated large number of X i Y i points per unit area (N [ 000), a mean NND R equals 5 : M R ¼ 0:5N 0:5 Eqn: and the expected variance (V R ) is given by: V R ¼ ð4 pþ= ð4pnþ Eqn: 2 Based on ratios of experimentally observed (index C ) and expected from random distribution (index R ) means (Q = M C /M R ) and variances (R = V C /V R ), it is possible to distinguish between random set (Q *, R * ), shortrange ordered set (Q [, R \ ), cluster set (Q \, R \ ), and set of cluster with a superimposed background of random points (Q \, R [ ). 5 A spatial distribution quadrant was suggested in this communication to present different possible structures. Figure 2 illustrates the different computationally generated virtual distributions of points (random, ordered, clustered, and combination of clusters with random) in the unit area and calculated for them Q and R ratios using Eqns. and 2. Results and Discussion In this communication, the application of described spatial distribution analysis was done for two cases. 744 International Journal of Metalcasting/Volume, Issue 4, 207

5 Figure 2. A spatial distribution quadrant with different virtual structural distributions and calculated mean NND (Q) and variance (V) ratios for these structures (black points). Case describes the effect of a cooling rate on graphite nodule spatial distribution. This effect was verified using continuously cast, large-diameter bars (200 mm) made from a near-eutectic composition un-alloyed ferritic pearlitic ductile iron. The melt was treated with FeSiMg and inoculated with FeSiBa before being poured into the launder of a continuous cast machine. Samples were taken at two radial locations: () near surface, where the melt was rapidly cooled in contact with a water-cooled graphite mold, and (2) from the middle of the cast bar, where ductile iron slowly solidified from the liquid core outside the mold. The graphite nodule count was 420 mm -2 for the nearsurface-located specimen and 93 mm -2 for the specimen taken from the bar center. The Voronoi tessellations for the near-wall and the center structures are shown in Figure 3. The nodule density populations and Voronoi cells differ for these structures. For each specimen, nodules were sorted into three groups (small, medium, and large) with an equal number of nodules in each group. After that, NND C was calculated for each group and compared to the theoretical outcome for random distribution assuming the same nodule number in both cases (Figure 4). Two differences can be mentioned; the mean NND C of the surface specimen was half that of the specimen from the center of the bar (red arrows), and Figure 3. Voronoi tessellations of graphite nodule distribution in 8 00 continuously cast bar: (a) near the center and (b) near surface ( mm 2 area). International Journal of Metalcasting/Volume, Issue 4,

6 0.8 Small 0.8 Probability Medium Large Random Probability Small Medium Large Random NND, micron (a) NND, micron (b) Figure 4. Spatial distributions of graphite nodules in 8 00 diameter continuously cast bar: (a) near surface and (b) near the center. Figure 5. Spatial distribution quadrant for graphite nodules in two specimens (rapid cooled near surface and slow cooled from center) of 8 00 diameter continuously cast bar. the former structure had larger departures of spatial distributions from a random distribution. A suggested spatial distribution quadrant (Figure 5) characterizes the nodule graphite structure using only one point with coordinate X (ratio of means of experimental to random NND) and Y (ratio of variances). These two numbers represent a possible departure of random structure from ordered of clustered spatial distributions. In this particular case, small graphite nodules had the tendency of clustering, while larger nodules had a more ordered spatial configuration. High cooling rates had no significant effect on the type of spatial distributions of nodules. Case 2 describes the effect of inoculation on the graphite nodule spatial distribution in two laboratory produced keel blocks with 5 mm wall thickness from hypo-eutectic pearlitic ductile iron treated in the ladle by FeSiMg. The first casting was poured without additional inoculation ( base ductile iron), and the second one was treated by Ba- and Ca-bearing FeSi inoculant ( inoculated ). Inoculation had a large effect on the graphite nodule number per unit of area (67 mm -2 in base vs. 306 mm -2 in inoculated irons). The Voronoi tessellations for the base and the inoculated structures are shown in Figure 6. Both the nodule density populations and Voronoi cells differ significantly for these structures. The same procedure of nodule classification into three equal groups (small, medium, and large) for each specimen was used to calculate the NND C distributions (Figure 7). Ductile iron inoculation slightly decreased a mean value of 746 International Journal of Metalcasting/Volume, Issue 4, 207

7 Figure 6. Voronoi tessellations of graphite nodule distribution in keel-block sand castings: (a) base and (b) inoculated ductile irons ( mm 2 area) Probability Small Medium Large Probability Small Medium Large Random Random NND, micron NND, micron (a) (b) Figure 7. Spatial distributions of graphite nodules in keel-block sand castings (a) base (b) inoculated ductile irons. Figure 8. Spatial distribution quadrant for graphite nodules in base and inoculated keel blocks produced in sand molds. International Journal of Metalcasting/Volume, Issue 4,

8 NND C while having a large effect on the shape of the curves which reflected changes in the type of spatial distribution. These changes are presented in the spatial distribution quadrant (Figure 8). In the base casting, the small graphite nodules had cluster superimposed on random distribution, while the medium-size nodules had near-random distribution; also, the large nodules exhibited ordering tendency. Inoculation significantly eliminated clustering of small graphite nodules. This can be a result of effective heterogeneous nucleation of graphite nodules during whole solidification period including forming a second nucleation wave which was described by the author. 7, Continuous graphite nodule nucleation will restrict the growth of large austenite dendrites and decrease the clustering tendency of small graphite nodules formed at the end of solidification. Conclusions Comprehensive characterization of the structure of nodular cast iron offers two advantages; the characterization results can be used for casting quality control, and useful casting solidification kinetics could be extracted. The microstructure characterization parameters which are used today mainly describe graphite morphology (shape, size, and number of graphite particles) as well as a metal matrix structure (ferrite/pearlite ratio for example). These methods were originally described by Saltikov, 8 De Hoff, 9 and the other authors. In this communication, the analysis of the spatial distribution of graphite nodules was discussed as an additional tool for complex structure characterization. The center-tocenter near-neighbor distance was used as a parameter for the global characterization of the type of spatial distribution which can be related to a nucleation event during solidification. The other parameter, surface-to-surface nearneighbor distance can also be used to characterize the local spatial distributions and link to crack propagation during failure and material s properties. 20 The suggested practical method for presentation of graphite nodule spatial distribution is based on plotting spatial quadrants with coordinates related to the ratio of measured NND to NND of randomly distributed particles. This method was used to characterize clustering or ordering tendencies. It was shown that the cooling rate and inoculation have significant effects on the type of graphite nodule spatial distribution. The results can be used for analysis of the solidification sequence and for casting quality control. REFERENCES. ISO 945, Microstructure of cast irons test method for determining nodularity in spheroidal graphite cast irons 2. A. De Santis, O. Di Bartolomeo, D. Iacoviello, F. Iacoviello, Int. J. Comput. Vis. Biomech. (2), (2008) 3. P. Prokash, V. Myrti, P. Hiremath, Int. J. Adv. Sci. Tech. 29, 3 40 (20) 4. S. Lekakh, J. Qing, V. Richards, K. Peaslee, Trans. Am. Found. Soc. 2, (203) 5. S. Lekakh, V. Thapliyal, K. Peaslee, in AISTech Proceedings (203), pp S. Lekakh, M. Harris, Int. J. Metal Cast. 8(2), 4 49 (204) 7. S. Lekakh, B. Hrebec, Int. J. Metal Cast. 0(4), (206) 8. M. Harris, O. Adaba, S. Lekakh, R. O Malley, V. Richards, in AISTech Proceedings (205), pp C. Basak, A. Sengupta, Scr. Mater. 5, (2004) 0. K.M. Pedersen, N.S. Tiedjie, Mater. Charact. 59, 2 (2008). S. Lekakh, ISIJ Int. 56(5), (206) 2. K.V. Makarenko, Met. Sci. Heat Treat. 5( 2), (2009) 3. P.P. Bansal, A.J. Ardell, Metallography 5, 97 (972) 4. V. Benes, R. Lechnerova, L. Klebanov, M. Slamova, P. Slama, Mater. Charact. 60, (2009) 5. W.A. Spitzig, J.R. Kelly, O. Richmond, Metallography 8, (985) 6. S. Kumar, S. Kurtz, Mater. Charact. 3, (993) 7. ImageJ 2 software, 8. S. Saltykov, Stereometric Metallography, 2nd edn. (Metallurgizdat, Moscow, 958) 9. R. De Hoff, Quantitative Metallography in Techniques of Metals Research, vol II, Part (Interscience, New York, 968) 20. L. Morales-Hernandez, A. Herrera-Navarro, F. Manriquez-Guerrero, H. Peregrina-Barreto, I. Terol-Villalobos, in International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing ISMM 20, pp doi: 0.007/ _ International Journal of Metalcasting/Volume, Issue 4, 207