Material
The characterization techniques presented in this paper are applied to a hypoeutectic LGI with approximate chemical composition as outlined in Table 1. From this base material, four versions (A, B, C, and D) were produced in portions of 400 g using remelting, isothermal heat treatment, and interrupted solidification as described by Hernando [19] resulting in cylindrical specimens of approximate dimensions Ø40 × 40 with dendritic microstructure of varying coarseness. The solidification of material A was interrupted soon after dendrite coherency at 1180 °C, marking the “0” reference point for the isothermal heat treatment. Material B, C, and D underwent a similar procedure, but instead of immediate quenching they were subjected to an isothermal heat treatment at 1168 °C for durations according to Table 2.
Table 1 Chemical composition of material
Table 2 Duration of isothermal ripening heat treatment
Sample Preparation and Etching
The specimens were cut in half perpendicular to the central axis using a wet abrasive cutter and were cold-mounted in Ø50 mm epoxy pieces. Porosities and cracks were filled with the resin as good as possible to facilitate subsequent preparation. After having cured for at least 12 h, the samples were ground individually by manual application to a water-lubricated SiC foil #80 on a rotating disk until planar and subsequently transferred to an advanced sample preparation system where it was ground on a #220 resin bonded diamond disk with water lubrication until deep scratches appeared gone followed by a composite disk with a 9 µm diamond suspension for 2 min and 30 s. The specimen was then polished on a taffeta woven wool cloth disk with a 3 µm diamond suspension for 3 min and finalized on a synthetic short napped cloth with the DiaPro Nap B 1 µm diamond suspension for 2 min and 20 s with 5 N force applied. If the material is similar and the sample preparation so far described is followed (with emphasis on the last polishing step), the surface should at this point already appear etched. If required, additional polishing time with the DiaPro Nap B 1 µm diamond suspension was added in increments of 20 s until the coloration appeared similar to Fig. 1 or the γ-dendrites were otherwise clearly distinguishable. If the martensite in the dendrites were significantly darkened such that it was judged to interfere with the subsequent measurements, it was in this investigation considered overetched and was reprepared for a new trial.
Microscopy and Selection of ROI
Using an optical microscope, the full Ø40 mm surface of the specimen was captured digitally in a systematic manner at 2.5× magnification using at least 1/3 overlap, making a total of about 200 micrographs per specimen that were stitched into a large single panorama image. On each panorama, four 5 mm × 5 mm sample regions were selected for analysis on the basis of avoiding defects and areas where dendrites were less clear.
Measured Microstructural Parameters
The microstructural object of interest in this article is the γ-dendrites, also often referred to as primary γ [10, 20–22] to emphasize that it is mostly known to form prior to the eutectic. This structure is however difficult to quantify directly while the material is still semi-solid and is instead studied at room temperature after rapid quenching. In the process of quenching, the liquid transforms into fine ledeburite termed “quench ledeburite,” while the γ-dendrites transform to martensite. It is in this study assumed that the shape of the martensitic dendrites closely resembles and is representative of the γ-dendrites microstructure before quenching and will from here on be referred to simply as “γ-dendrites.” Two parameters will be considered: the volume fraction V
V and the specific surface area S
V of the γ-dendrites. These parameters were selected for their importance in modeling of solidification phenomena [23–25] and are sufficient to derive other meaningful parameters such as the modulus of dendrites M
D and the hydraulic diameter of interdendritic phases \( D_{\text{IP}}^{\text{Hyd}} \) [6].
Stereology
The microstructural parameters V
V and S
V are both volumetric properties; however, using stereological relationships, they can be derived from various types of measurements on a cross section of the specimen. Table 3 which has been adopted from Underwood [26] provides an overview of these relations. Studying the table it is realized that the two parameters are not stereologically connected and require separate measurements. V
V (volume fraction) can be calculated from P
P (point fraction), L
L (line fraction) or A
A (area fraction) and S
V (surface area per unit volume) can be calculated from L
A (line length per unit area) or P
L (intercept points per line length). This article focuses on a computer-aided A
A-based method for measurement of V
V and a L
A-based method for measurement of S
V but a comparison to P
P and P
L will be made which is further described later in this article. V
V, A
A, and P
P are equivalent \( V_{\text{V}} = A_{\text{A}} = P_{\text{P}} \). S
V is however not equal but proportional to L
A and P
L and must be calculated using
$$ S_{\text{V}} = 4/\pi *L_{\text{A}} = 2P_{\text{L}} $$
(1)
as described in [26].
Table 3 Stereological relations
Adobe Photoshop Quantification (PSQ)
The utilized image analysis software offers many useful tools for quantification of the microstructure. To identify phases, the software allows the user to interactively try to define a color filter such that pixels that are associated with the phase of interest are considered. The software then identifies isolated aggregates that can be considered as separate entities. A second filter can be applied to consider only aggregates that possess certain shape characteristics. It was however not possible to quantify the dendritic microstructure with satisfactory accuracy using these tools. Some researchers have turned to commercial [19, 27] as well as open source [28] raster graphics editors for alternative means of quantifying the microstructure. A popular approach is to draw the microstructural features by hand using, e.g., a mouse or a tablet [6, 19, 28], although some have experimented with a variety of selection tools [27]. In this study, it was attempted to use the Adobe Photoshop software’s more advanced selection tools to find a quicker technique to produce a representative selection. In line with Zhang et al. [27], this method will be referred to as Adobe Photoshop Quantification (PSQ). It has been noted that it is possible to find the number of selected pixels directly from the Adobe Photoshop software making it possible to calculate, e.g., the volume fraction [27], but to measure the perimeter of the selection, a complementary image analysis software is required. In order to combine the selection capabilities of the Adobe Photoshop CS6 software with the quantification capabilities of an image analysis software, the selected areas were filled with a single color that was in clear contrast with the background, producing a more easily interpreted analogue image of the microstructure which will be referred to as microstructure analogue. This process will now be explained in more detail. The selected portions of the panorama micrograph were exported to separate files and opened in the Adobe Photoshop CS6 software. The image was copied to a new layer on top of the original, and the color blending mode of the layer was changed to multiply. A vector mask was added to the layer. Using Color Range in the properties of the vector mask, the dendrites were masked on the basis of color. The fuzziness parameter of the color range was set to zero, meaning the selection will consist exclusively of exactly the colors that have been sampled. The colors included in the selection were defined by panning around the image at high magnification and sampling colors from the dendrites on the micrograph until all the colors that were associated with dendrites had been selected. A selection was then produced using the “Load Selection from Mask” function. Because the colors of the γ-dendrites were also found in other parts of the microstructure, the selection at this point typically looked like Fig. 2(a and b) which is not a satisfactory representation of the structure. The selection was modified using the “Contract Selection” tool which retracts the boundary of the selection by a specified number of pixels. For the finest dendritic structure (specimen A), this corresponded to 3 or 4 pixels. For specimen B, C, D, up to 6 pixels were contracted. After this step, the selection looked like in Fig. 2(c and d). Note how most of the undesired details in the space between the dendrites have been removed, but that the boundary of the dendrites is also contracted. The selection was then restored to the boundary of the dendrites using the “Expand Selection” tool by an equal number of pixels as it was contracted (Fig. 2e and f). Due to the nature of the contraction and expansion procedure, the resulting boundaries are crude. To make the boundary more representative of the dendrites, the “Smooth Selection” tool was applied for 2 pixels (Fig. 2g and h). A new layer was created on top of the others, and the selected area was colored using the fill function.
While the microstructure analogue now appears more representative of the dendritic structure, close inspection reveals discrepancies. To assess the error induced by these residual flaws, another microstructure analogue was produced where the flaws had been manually corrected using the lasso tool. Errors that were corrected included dendrites that had not been selected, selections that did not contain dendrites, separate dendrite arms that had been selected as one, intradendritic particles that were not selected, selection of eutectic cells, selection of the smaller set of dendrites that formed during quenching, and selection boundaries that were obviously offset from the actual dendrite boundaries. In cases where it was not clear whether the selected features were dendrites, no alterations were made. A new layer was created on top of the others, and the selection was colored in the same manner as before, but now with a different color. By changing the layer’s blending option to multiply, it was possible to produce an image where the two selections as well as their intersection could be displayed in one image as can be seen in Fig. 3. This enabled distinction between removed, added, and retained area which would help in later analysis. A plain white layer was created between the colored layers and the masked layer to serve as background. The multi-layer file was saved using the software’s standard PSD format for backup and the JPEG format with maximum quality as input for the image analysis software where the parameters were measured. The image analysis software was set up to measure the full areas and their perimeters. This means that objects on the boundary of the image were included and that no object shape filters were applied. It has been pointed out [27] that if only the area is of interest, the pixel count of a selected area can be counted directly in the Adobe Photoshop software using the histogram with expanded view. Note that to count the true number of selected pixels it may be necessary to make an “Uncached Refresh” using the button found in the top right of the histogram window. All measurements were made in the units of pixels. V
V is unitless and in no need of conversion, however, S
V is of reciprocal length and needs conversion to a more meaningful unit. An easy way to find the relation between pixels and metric units is to simply produce a micrograph with equivalent magnification that includes a metric ruler, then measure the number of pixels across the ruler. In this study, 1 mm corresponded to 460 pixels.
Line Intercept and Point Counting Methods
The PSQ measurements were compared to reference measurements acquired using line intercept and point counting methods. These measurements were performed on the raw micrographs of the same sample regions. Underwood’s [26] recommendation to use circular test lines for measurements of PL on highly oriented structures appears reasonable. Three concentric circles of Ø2 mm, Ø3 mm, and Ø4 mm were superposed over the center of each sample region. A cross was placed in the center to divide the circles into four equal quadrants. The number of dendrite boundary intersection points was counted separately in each quadrant. In cases where the line appeared to tangent a boundary but it was unclear whether it crossed or not, it was counted as 1 point (as opposed to both an entry and an exit point). P
L was calculated as P
L = P
Ø
/L
Ø
where P
Ø
is the number of intercept points and L
Ø
is the sum of circumferences of the circles superposed over the micrograph. S
V was then calculated from P
L according to (1) and will be referred to as S
PLV
. P
P was measured using a 22 × 22 square grid (484 points) superposed over the sample regions. Each point was counted as on a dendrite, on its boundary or not on a dendrite. Boundary points were counted separately and weighted as ½ a point in the calculation to reduce subjectivity as recommended by [26]. P
P was calculated as \( P_{\text{P}} = (P_{1} + \raise.5ex\hbox{$\scriptstyle 1$}\kern-.1em/ \kern-.15em\lower.25ex\hbox{$\scriptstyle 2$} P_{\raise.5ex\hbox{$\scriptstyle 1$}\kern-.1em/ \kern-.15em\lower.25ex\hbox{$\scriptstyle 2$} } )/P_{\text{T}}, \) where P
1 is the number of points that fell over a dendrite, P
½ is the number of boundary points, and P
T is the total number of points in the grid.
Evaluation
The etching technique is evaluated qualitatively by looking at what microstructural components are colored and how representative the revealed patterns are of the γ-dendrite shape and area or how useful it is for quantification of the structure. The evaluation of the PSQ method will concern the accuracy of the produced microstructure analogues with respect to the two considered microstructural parameters V
V and S
V. The true values of the parameters are unknown, so to evaluate the method, its agreement to a reference method is instead analyzed. In this case, the reference methods for volume fraction and surface area measurements are the point counting and line intercept methods, respectively. The agreement analysis is inspired by Bland and Altman [29] who suggest plotting the differences between the measurements of two methods against their average values. If there is a significant mean difference between the methods, there is a systematic disagreement. The sample standard deviation of the differences on the other hand provides information about scatter or random disagreement. The differences are in this case obtained by \( d = x - x_{\text{ref}}, \) where \( x_{\text{ref}} \) is a reference measurement and \( x \) is the corresponding PSQ measurement. An advantage of this method is that it helps discern between what is actual variation in the properties of the different sample regions and materials and the variation induced by the measurement methods. The mean of the differences is calculated as
$$ \bar{d} = \frac{{\mathop \sum \nolimits_{i}^{n} (d_{i} )}}{n}, $$
(2)
where the mean is denoted by bar accent, i is the index of the source micrograph the measurement was performed on, and n is the sample size. The sample standard deviation of the differences \( s \)(\( d \)) is calculated as
$$ s\left( d \right) = \sqrt {\frac{{\mathop \sum \nolimits_{i}^{n} \left( {d_{i} - \bar{d}} \right)^{2} }}{n - 1}} $$
(3)
The significance of the mean difference is tested by calculating the sample standard deviation of the mean i.e., the standard error \( {\text{SE}}(d) = s\left( d \right)/\sqrt n \) and then calculating the 95% confidence interval for the mean \( {\text{CI}}_{95\% } \left( {\bar{d}} \right) = \bar{d} \mp 1.96*{\text{SE}}(d) \). If CI95% does not contain zero, the mean difference is considered significant meaning there is likely a systematic disagreement. It is also useful to consider the expected scatter of the reference methods. For the point counting method, the sample standard deviation is calculated as
$$ s\left( {P_{\text{P}} } \right) = \frac{1}{{\sqrt {n_{\text{T}} } }}\sqrt {\frac{{\left( {n_{1} \left( {1 - \bar{P}_{\text{P}} } \right)^{2} + n_{\raise.5ex\hbox{$\scriptstyle 1$}\kern-.1em/ \kern-.15em\lower.25ex\hbox{$\scriptstyle 2$} } \left( {\raise.5ex\hbox{$\scriptstyle 1$}\kern-.1em/ \kern-.15em\lower.25ex\hbox{$\scriptstyle 2$} - \bar{P}_{\text{P}} } \right)^{2} + n_{0} \left( {0 - \bar{P}_{\text{P}} } \right)^{2} } \right)}}{{n_{\text{T}} - 1}} }, $$
(4)
where n is the total number of points, n
1, n
½, n
0,, and n
T are the number of points on a dendrite, on its boundary, not on a dendrite, and the total number of points, respectively. Note that since P
P is the mean of the 484 points, s(P
P) is equivalent to the standard error of the mean of the points leading to Eq. (4). The sample standard deviation of S
PLV
is calculated from the subtotal S
LAV
of the four quadrants of the test circles
$$ s\left( {S_{\text{V}}^{\text{PL}} } \right) = \sqrt {\frac{{\mathop \sum \nolimits_{i = 1}^{4} \left( {S_{{{\text{V}}i}}^{\text{PL}} - \bar{S}_{\text{V}}^{\text{PL}} } \right)^{2} }}{n - 1} } $$
(5)
and the standard error simply \( SE\left( {S_{\text{V}}^{\text{PL}} } \right) = s\left( {S_{\text{V}}^{\text{PL}} } \right)/\sqrt 4 \). The measurements obtained from the manually corrected analogues are compared against PP and S
PLV
in an identical fashion, and the two analyses are compared side by side to determine the fruitfulness of the corrections. To discern between the two, measurements on the manually corrected analogues are denoted by an asterisk (*). The sample standard deviation of the measurements on the analogue was then estimated as
$$ s\left( x \right) = \sqrt {s\left( {x - x_{\text{ref}} } \right)^{2} + s\left( {x_{\text{ref}} } \right)^{2} }, $$
(6)
where x is the PSQ measurement and x
ref is the reference measurement.