DE102014101134A1 - Automatically quantifying dendrite arm spacing in hypoeutectic aluminum casting, comprises acquiring image by computer-based system, estimating dendrite cell size and aspect ratio of dendrite cell, and converting into dendrite arm spacing - Google Patents

Abstract

Automatically quantifying dendrite arm spacing in a hypoeutectic aluminum casting, comprises: acquiring an image corresponding to a location of interest in the casting by using a computer-based system; estimating a dendrite cell size within the location of interest using the computer-based system; using the computer-based system to estimate a volume fraction of eutectic phases; estimating an aspect ratio of at least one dendrite cell; and converting the estimated dendrite cell size volume fraction and aspect ratio into a quantified dendrite arm spacing using the computer-based system. Automatically quantifying dendrite arm spacing in a hypoeutectic aluminum casting, comprises: acquiring an image corresponding to a location of interest in the casting by using a computer-based system; estimating a dendrite cell size within the location of interest using the computer-based system by overlaying the image with a gridded pattern to quantify the number of intersections between at least one cell boundary from the image and at least one portion of the gridded pattern; using the computer-based system to estimate a volume fraction of eutectic phases in the location of interest; estimating an aspect ratio of at least one dendrite cell within the location of interest by using the computer-based system; and converting the estimated dendrite cell size volume fraction and aspect ratio into a quantified dendrite arm spacing using the computer-based system. Independent claims are also included for: (1) automatically quantifying dendrite arm spacing in a hypoeutectic aluminum casting, comprising selecting a cast material to be analyzed, using an image analyzer to automatically determine dendrite cell size information corresponding to a location of interest in the selected cast material, and converting the dendrite cell size information to dendrite arm spacing information; (2) using an image analyzer to automatically determine dendrite cell size information comprises acquiring an image corresponding to location of interest, and estimating dendrite cell size within the location of interest by overlaying the image with a gridded pattern to quantify the number of intersections; and (3) an article of manufacture comprising a computer usable medium having computer readable program code embodied in it for automatically quantifying dendrite arm spacing for a hypoeutectic aluminum cast material, in which computer readable program code in the article of manufacture comprises computer readable program code portion for causing the computer to accept data pertaining to digital information of a location of interest within a sample of cast material, computer readable program code portion for causing the computer to process digital information into dendrite cell size information, computer readable program code portion for causing computer to convert dendrite cell size information into a corresponding dendrite arm spacing through an algorithm based on a volume fraction of eutectic phases in location of interest and an aspect ratio of at least one dendrite cell within location of interest, and computer readable program code portion for causing the computer to produce an output that corresponds to dendrite arm spacing.

Description

INDICATION OF RELATED CASES

This application is a partial continuation of U.S. application serial no. No. 13 / 757,914, filed February 4, 2013, entitled Method for Automatic Quantification of Dendrite Arm Spacing in Dendritic Microstructures claiming priority to US Provisional Application 61 / 623,145, filed April 12, 2012.

BACKGROUND OF THE INVENTION

The present invention relates generally to the quantification of the microstructural fineness of cast metal parts and, more particularly, to the automated quantification of dendrite arm spacing (DAS) in dendritic microstructures of metal castings as a method of avoiding manually making such measurements.

The resulting microstructure of all aluminum-based cast components (eg, engine blocks, cylinder heads, gearbox parts, or the like) is generally determined by the alloy composition, and more particularly by the solidification conditions. In hypoeutectic alloys (i.e., those containing less of the other alloying constituents than the eutectic composition, examples of which include, but are not limited to, A356 and 319), the materials tend to solidify dendritically. Other such aluminum alloy examples exemplifying dendritic solidification include 354, 355, 360, 380, 383 and others. A typical microstructure of this family of alloys consists of a primary dendritic phase and a second phase of particles such as, for example. As silicon particles and iron-rich intermetallic compounds. The relative amounts, sizes, and relative morphology of these phases in the as-cast structure are highly dependent on the solidification conditions as well as the alloy composition. The dendrite cell size (DCS) and DAS, sometimes referred to as the secondary dendrite arm spacing (SDAS), have long been used to quantify the fineness of the casting, which in turn can be used to gain a better understanding of the material and its associated properties in which, as a general rule, cast components with a smaller DAS tend to have better ductility and similar mechanical properties. Explanations regarding aluminum alloy casting in general, as well as the DAS characteristics in particular, are found in numerous other patent applications owned by the assignee of the present invention, and include U.S. Patent Application 12 / 356,226, filed 20. U.S. Patent Application 12/402 538 filed 12 March 2009, U.S. Patent Application 12/454 087 filed May 12, 2009, and U.S. Patent Application 12/932 858 filed March 8, 2011, all of which are hereby incorporated by reference are hereby incorporated by reference.

Many efforts have been made to describe dendrite refinement and its relationship to solidification conditions, starting in 1950 with Alexander and Rhines, who were the first to provide a quantitative basis for the influence of composition and rate of solidification for certain dendrite features. Table 1 summarizes the known literature for the description of the fineness of a dendritic structure in quantitative terms. TABLE 1 Microstructural parameters for the description of dendrites

Of these have Spear and Gardner (1963) The scale of a dendritic structure is quantitatively described by means of the DCS, which is obtained by a random line intersection and in its 3 (a) is called DCSli. Following Spear and Gardner Jaquet and Hotz (1992) in their study also the DCS _li ; used to quantify the dendrites. Levy et al. (1969) . Oswalt and Misra (1980) . Radhakrishna et al. (1980) and Flemings et al. (1991) have all debated DAS to quantify the dendritic structure. In these approaches, DAS is obtained by an intersection technique, where the line is chosen to intersect a series of well-defined secondary dendrite arms.

McLellan (1982) used the dendrick cell number (CPUA) to quantify the microstructure and claims that it describes the deformation process more accurately than the DAS. However, had Levy et al. (1969) critically analyzed the measurements of both the DAS and the CPUA to characterize the cast structure and noted that the standard deviation for the DAS measurement was smaller than for the CPUA measurement, and also the mean cell size calculated from the CPUA is larger as the middle DAS. The measurement of the CPUA includes primary, secondary, and tertiary arms of the dendrites, whereas DAS measurements usually refer only to the secondary arm distance.

The methods associated with the manual measurement of DAS have often been used by the applicant of the present invention as a method of performing a DAS measurement of aluminum castings. Such a procedure generally includes first preparing metallographic samples prepared in accordance with known standards such as the American Society of Testing and Materials Standard Guide for Preparation of Metallographic Specimens (also known as ASTM E3 known), a portion of which is shown in Table 2 below. TABLE 2 ASTM E3 surface lubricant Type / size of abrasive ANSI (FEPA) time in seconds Force ^A in N (lbf) Speed ^{B of} the roller rotation Surface grinding paper / stone water SiC / Al ₂ O ₃ with grain size 120-320 (P120-400) 15-45 20-30 (5-8) 200-300 ^C CO ^D Fine grinding firm nylon cloth Compatible lubricant 6-15 μm diamond 180-300 20-30 (5-8) 100-150 CO Coarse polishing cloth with low / no pimples Compatible lubricant 3-6 μm diamond 120-300 20-30 (5-8) 100-150 CO Final polishing cloth with medium / high nubs Compatible lubricant 1 μm diamond 60-120 10-20 (3-5) 100-150 CO Art suede ^E water 0.04 μm colloidal silica or 0.05 μm alumina 30-60 10-20 (3-5) 100-150 CON TRA ^{F A} force per 30 mm (1¼ inch) diameter of fixture.
^B drive heads generally rotate at a speed between 25 and 150 rpm.
^C high speed stone grinders generally rotate at a speed greater than 1000 rpm.
^D Complementary rotation, surface and sample rotate in the same direction.
^E Optional step.
^F Opposing rotation, surface and sample rotate in the opposite direction.

The surface of the sample to be analyzed is expected to be of sufficient quality to reflect the true size and shape of the particles as much as possible. In one form, the plane of the cut will include eutectic phases that appear darker compared to the surrounding matrix. Thus, the metallographic samples are finally ground in a mold to obtain a flat, nearly mirror-image surface finish. Chemical etching may be used to increase the contrast of the dendrite structure, etching in a mold in accordance with ASTM E407 can be done. The sample is preferably clean and dry, while abrasive artifacts (eg, comet tailing, pitting, scratching, tearing, and staining) should be kept to a minimum. Similarly, test conditions and deviations should be preceded by agreement. In a preferred form, each sample is examined in many fields of view, each of which is subjected to a high magnification (eg, 100 times), which depends on the fineness of the grain of material. Then an image of the field of view to be measured should be taken. In one form, the line-cut method can be used to measure DAS, with three or more dendrites with visible dendrite trunks selected with at least three dendrite arms per field of view. From this, a line is drawn from the outer edge of the first dendrite arm to the inner edge of the last dendrite arm; an example of this is in 6B displayed. The distance d for each dendrite may be recorded while the number n1, n _2, n _3, etc. of the dendrite arms, which are counted for each measurement, can also be recorded. These processes can be repeated for each field of view.

Currently, both percent volume of eutectics and DCS can be automatically determined using an image analyzer. The local cooling rate does not only affect the microstructural fineness, but also the pore formation. For this reason, there is a tendency to use the DAS more frequently to quantify the microstructural fineness. The problem with measuring the DAS is that it must be done manually by identifying the well-defined dendrite arms in the image. Unfortunately, this is both very time consuming and heavily dependent on the skill of the user or individual performing the measurement.

SUMMARY OF THE PRESENT INVENTION

The inability to automatically account for DAS and similar material property spreads of cast components is overcome by the various aspects of the present invention herein disclosed, wherein robust, accurate and automatic measurements of DAS in dendritic microstructures of metal castings for product quality control as well as product performance and durability analysis can be used. In accordance with one embodiment, a method for automatically predicting a distribution of DAS and similar material properties within a casting component is disclosed. The method includes taking a microsample from the casting site of interest. In the present context, a microsample is a metallographic sample prepared in a standard microstructural analysis protocol. Such a standard working instruction includes that of the one explained above ASTM E3 , Then the sample is analyzed. In one form, an image analyzer may be used to measure DCS using either a DCS line (DCS _li ) method or a mean area equivalent circle diameter method (DCS _ed ) or a similar approach using a DCS, a eutectic volume fraction, and a dendrite Aspect ratio can be used. From this, the measured DCS value is converted to a DAS in accordance with one of the relationships set forth herein. The quantified DAS value, which corresponds to the DCS value, is preferably converted into a user-ready format, such as a. For example, a human-readable phrase or data may be rendered in a computer-readable format that may then be manipulated via a computer print device, computer-readable algorithm, or other suitable means. In a preferred form, an average of the various aspect ratios measured within a particular point of interest may be used as a representation of the aspect ratio in a subsequent calculation or similar algorithm.

As currently configured, automation may be through a program or similar algorithm that may be running on a digital computer or otherwise executed to produce the resulting DAS data representation. In a preferred form, the digital computer preferably includes one or more of an input, an output, a processing unit (often referred to as a central processing unit (CPU)) and a memory temporarily storing such a code, program or algorithm in the memory of the computer or permanently store so that the instructions contained in the code can be acted upon by the processing unit based on input data so that the output data generated by the code and the processing unit can be forwarded via an output to another program or another user. In one form, a portion of the memory (also called operational memory) containing data is referred to as random access memory (RAM), while a portion of the memory (also referred to as persistent memory) containing instructions is referred to as a read-only memory (ROM). A data bus or similar set of wires and associated circuitry form an appropriate data transmission path that can interconnect the input, output, CPU, and memory, as well as any peripherals, in such a way as to allow the system as one integrated whole to work. Such a computer system is referred to as having a Neumann architecture (also referred to as a general purpose or programmable memory computer). Likewise, a specially adapted computer or computer-like computing device which uses the typical features of a Neumann architecture to perform at least some of the data acquisition, data manipulation, or similar computational functions is considered to be within the scope of the present invention.

In accordance with another embodiment, a method for automatically predicting a distribution of the DAS and similar material properties within a cast component is disclosed. The method includes selecting a cast material to be analyzed, using an image analyzer to automatically determine DCS information corresponding to the selected material, and then present the DCS information by at least one of (a) an empirical relationship or (b ) of a theoretical relationship between the DCS and DAS information is converted into DAS information.

In accordance with another aspect of the present invention, an article of manufacture is disclosed. The article includes a computer-usable medium having computer-readable program code embodied therein for quantifying DAS properties of a casting material so that it may be used in a general-purpose computer or a specially adapted computer as discussed above. Specifically, such computer readable program code includes a section for causing the computer to accept or read data corresponding to digital information taken from a sample of the molding material, as well as a section to cause the computer to display the information to DCS information, in addition to a section to convert the DCS to DAS based on either a theoretical relationship between these two forms of information or an empirical relationship. Moreover, the program included in the computer-readable medium includes a section for outputting the DAS information. Such an output may be in machine-readable or human-readable form. In this way, the determination of DAS contained in the computer-usable medium is carried out in an automated manner, thereby avoiding the need to manually capture such information, as explained above. In a more specific form, the use of an image analyzer (or similar device) to automatically determine dendrite cell size information corresponding to the selected casting material may be achieved by using either the DCS _li , DCS _ed , or the algorithmic combination of eutectic volume fraction and dendrite aspect ratio, such as explained above. Further, a site to be analyzed may be prepared using a standard metallographic technique (such as that described above ASTM E3 corresponding). In a particular form, the digital information that is read or otherwise assumed is in the form of digital images, such as digital images. For example, those may be scanned by the image analyzer, devices interacting with the image analyzer, or other methods known to those skilled in the art. In another specific form, the casting material may be an aluminum alloy in general and a hypoeutectic aluminum alloy in particular.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of specific embodiments is best understood when read in conjunction with the following drawings in which like structures are designated by like reference numerals and in which:

1 Figure 4 is a graph showing the disclosed DAS and DCS relationships empirically and theoretically (with varying percentages of the eutectic phase volume fraction in a local microstructure) along with the measured DAS and DCS values for two different cast aluminum alloys 319 and A356;

2A to 2E show how an empirical and a theoretical relationship between DCS and DAS can be made over, inter alia, a percent eutectic volume fraction based on an image of a microstructure site of interest;

3A shows a photomicrograph of an aluminum casting alloy, wherein the line-cut method is used to determine the size of the dendritic structure such. B. DCS _li and DAS according to the prior art to measure;

3B a representation of the photomicrograph of 3A showing in greater detail a particular cell structure using a known semi-automatic technique, the length being divided by the number of dendrites;

3C the special dendrite cell structure of 3B which is subjected to an aspect ratio analysis;

4 shows the relationship between DAS and DCS for aluminum alloys A356 and 319 using an empirical relationship;

5 and 5A Flowcharts depicting the various steps for determining DAS according to the present invention;

6A and 6B show exemplary methods to achieve a DAS measurement; and

7 Figure 4 shows an image analysis system that may be used to automatically quantify a dendrite arm spacing according to one aspect of the present invention.

The embodiments set forth in the drawings are purely illustrative and not intended to limit the embodiments defined by the claims. In addition, individual aspects of the drawings and the embodiments will become more apparent and better understood in connection with the following detailed description.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

First of all, referring to the 1 and 7 As mentioned above, one of two approaches is used to convert a measured or detected DCS value to DAS. As discussed above, an automated method of predicting DAS distribution within a casting component may include taking a microsample from the casting station of interest and analyzing it by a computer-based image analysis device. With special reference to 7 includes an image analyzer system (also referred to herein as an image analysis system, image analyzer or the like) 300 a computer 310 or a similar data processing device, the or a processing unit 310A (which may be in the form of one or more microprocessors), one or more information input mechanisms 310B (including a keyboard, mouse, or other device such as a voice recognition receiver (not shown)) as well as one or more loaders 310C (which may be in the form of a magnetic or optical memory or similar storage in the form of CDs, DVDs, a USB port or the like), one or more display screens or similar information output 310D , a store 310E and computer readable program code means (not shown) for processing at least a portion of the received information regarding the aluminum alloy. As professionals will see, the memory can 310E in the form of a random access memory (RAM, also called large memory which can be used for temporary storage of data) and a memory storing instructions in the form of a read only memory (ROM). In addition to other forms of input that are not shown (such as an Internet or similar connection to an external data source), the loaders may 310C serve as a way to transfer data or program instructions from one computer-usable medium (such as a flash driver or the aforementioned CDs, DVDs or similar media) to another (eg, the memory 310E ) to load. As professionals will see, the computer can 300 as a separate (ie self-contained) entity, or it can be the part of a larger network, such as Those that are found in cloud computing where various computing, software, data access and storage devices may reside in very different physical locations. Such a separation of computer sources does not prevent such a system from being categorized as a computer.

In a special form, the computer-readable program code can be loaded into a ROM, which is part of the memory 310E is. Such computer readable program code may also be embodied as part of an article of manufacture such that the instructions contained in the code reside on a magnetically readable or optically readable disc or other similar nontranslational machine readable medium, such as the like. A flash memory device, CDs, DVDs, EEPROMs, floppy disks or other such medium capable of storing machine-executable instructions and data structures. On such a medium can from the computer 310 or other electronic device with the processing unit 310A which is used to interpret instructions from the computer readable program code. As professionals in the field of computer viewing can use a computer 310 which is part of an image analysis system 300 It also forms additional chipsets as well as a bus and similar wiring to pass data and related information between the processing unit 310A and other devices (such as the aforementioned input, output and memory devices). When the program code means is loaded in the ROM, the computer becomes 310 of the system 300 a special purpose machine configured to determine an optimal casting component in a manner described herein. In another aspect, the system can 300 only the instruction code (including that of the various program modules (not shown)) while the system 300 in yet another aspect, may include both the instruction code and a computer-readable medium as mentioned above.

Professionals will also realize that it is beyond the approach of manual input, which in the input 310B (especially in cases where large amounts of data are entered), other ways to receive data and similar information, and any conventional means of providing such data to the processing unit 310A permitting them to act on them is within the scope of the present invention. The input 310B as such, even in the form of a high data rate line (including the Internet connection mentioned above) may be present to store large amounts of code, input data or other information into the memory 310E to accept. The information release 310D is configured to provide information regarding the desired pouring approach to a user (if the informational output 310D z. In the form of a screen as shown) or to another program or model. Professionals will equally see that the input 310B and the issue 310D assigned characteristics to a single functional unit, such as B. a graphical user interface (GUI) can be combined.

The image analysis system 300 is used to get information from pictures 322 in particular using metallographic techniques, to relate the structure to the physical properties of the material of interest. These properties would include, but are not limited to, tensile strength, yield strength, elongation, and hardness. Starting with a prepared metallographic sample, a microscope becomes similar to a collapsed microscope 320 (but not limited to) uses a picture 322 to do that from the camera 330 is recorded. Typically, many pictures 322 through the use of a travel table 340 , often motorized, and a driving pattern 350 added. Then, on these digitized images 322 in a computer-based routine or algorithm 360 a gray threshold operation is performed to form the image analysis software stored in the memory 310E or any other suitable computer-readable medium. Then the routine measures 360 the threshold pixels of the image 322 , Thereafter, these data are analyzed to produce the final result. A travel table control unit 370 (using a joystick-like control) is used to move the microsample from one field to another field in the microscope 320 with the three-dimensional (Cartesian) x, y and z (focus) travel table movements through the travel stage controller 370 to be controlled. This allows movement across a travel table pattern to allow the analysis of many fields of view over the sample. This automated driving pattern - which includes autofocus features - allows you to capture large amounts of data in a short amount of time. The joystick of the travel table control unit 370 allows movement of the traversing table while passing the specimen through the eyepiece of the microscope 320 is observed in order to facilitate the selection of specific areas on which the analysis is performed.

With special reference to 1 In a first approach, an empirical formula is used, which was developed from the experimental data for different materials: DAS = a · DCS + b (1) where a and b are material constants while in a second approach a physically based (ie theoretical) equation is used: DAS = (1 - V _eu ) · DCS (2) where V _{eu is} the actual volume fraction of eutectic phases in a local microstructure. In the present context, since it is difficult and impractical to measure the actual volume fraction V _eu , a measured area fraction (which can be readily determined using the image analyzer) is used as an equivalent. It is clear that there is a small difference between the two, since the measured area fraction is a two-dimensional representation of the eutectic phase, whereas the volume fraction V _{eu is} a three-dimensional representation. Nevertheless, the measured area fraction provides sufficient accuracy (from a statistical point of view, if all particles of the eutectic phase are spherical, the measured area fraction corresponds to the volume fraction).

This second approach would provide robust and automated DAS measurements in dendritic microstructures of metal castings not only for product quality control but also for product performance and durability analysis. In the empirical or physically based (ie theoretical) approach of the above Eq. (1) and (2), DAS can be used in a dendritic structure of cast aluminum alloys, such as aluminum alloys. For example, A356 and 319 are determined automatically from the measurement of one or both of DCS and the eutectic volume fraction V _eu . Similarly, in Eq. (2) V _eu = k · f _eu , where f _{eu is} the theoretical volume fraction of the eutectic phase of the alloy (under equilibrium conditions) and k (> 1) is a coefficient for taking into account the increase in volume fraction of eutectic phases with increasing solidification (cooling) velocity ( ie with decreasing DAS).

The invention discussed herein is particularly well suited for hypoeutectic metals (ie, those having a dendritic structure). The present invention significantly aids in eliminating tedious, time-consuming manual measurement of DAS, as well as reducing operator-dependent errors, otherwise a relatively large standard deviation (typically on the order of ± 20%) may be present. This, in turn, provides more accurate and reliable DAS data, and also saves lab technician time and costs. In a special form, a microprobe is taken, which then automatically in an image analyzer 300 using DCS _li , DCS _ed or the algorithmic combination of the eutectic Volume fraction and dendrite aspect ratio as shown below in Eq. (5), analyzed for DCS. The measured DCS value is then converted to DAS in accordance with either the empirical or the physical based (ie theoretical) approach discussed above. Significantly, the method of the present invention would provide robust and automated measurements of DAS in dendritic microstructures of metal castings that can be used not only for product quality control but also for product performance and durability analysis.

Referring next to the 2A to 2D along with 5 steps in connection with the automatic determination of DAS using the DCS _li are shown (together with the dendritic cells aspect ratios). First, referring to 2A is a picture of the microstructure 10 shown in the photographed state showing a region to be analyzed. The microstructure 10 consists of primary aluminum dendrites 20 (white) and eutectic Si and Fe rich particles 30 (black) and eutectic aluminum 40 (this does not form dendrites, but instead occupies sites next to the eutectic particles 30 ). Referring next to 2 B become five concentric circles 50 of known length (shown in exemplary form as 500 microns) as a grid (or grid pattern) in an image analyzer system 300 (in 7 shown) used the DCS of the in 2A shown dendritic microstructure 10 estimate. The value of DCS is calculated using DCS = L / n (3) where L is the total length (circumference) of the five circles and n is the total number of intersections of the five circular lines that intersect at cell boundaries. It will be appreciated that other grating patterns, other than concentric circles, may be used as long as they provide for a simple determination of DCS in the above-mentioned linear fashion. Referring next to 2C are the concentric circles 50 from 2 B about the microstructure 10 from 2A placed while the image analyzer 300 DCS and the percentage volume fraction of the eutectic regions 30 and 40 measures. 2C also shows the procedure of automatic measurement of DCS in an image analyzer 300 , where the overlapped image with the five circles in the edited microstructure 10 in the image analyzer 300 stretched one or more times and then removed to the eutectic regions 30 . 40 fill completely to determine the number of segments (truncation). So then becomes the edited image of the microstructure 10 , as in 2C shown with the image from the concentric circles 50 in the image analyzer 300 combined to allow this to determine how many segments of the concentric circles 50 through the eutectic regions 30 . 40 be interrupted or cut. The area fraction of the eutectic regions 30 . 40 in a captured point of interest of the initial image of 2A as such, it is measured by converting the captured image to a eutectic image equivalent, where the ratio of the dark regions relative to the brighter regions gives a measure of the area percent of the eutectic. It can be seen that the dark region of 2C a fusion of the eutectic particles 30 and eutectic aluminum 40 equivalent.

Referring next to 2D becomes a graph that uses the equation given in Eq. (2) theoretical relationship automatically calculated DAS shows compared with a recorded or manually measured DAS. Each data point in 2D stands for both the manually measured DAS value from the horizontal axis and the calculated DAS value from the vertical axis. The solid line indicates that the calculated DAS value is exactly the same as the manually measured DAS. The two dashed lines are the average manually measured DAS within a standard deviation of the manually measured DAS value, and all of these data points are within this limit. Even better results were obtained by the more detailed analysis of Eq. (5), which will be explained in more detail below.

Referring next to 4 the relationship between DAS and DCS is shown, and a good linear correlation between the experimental data is evident. While 1 shows the theoretical relationship between DCS and DAS with different eutectic volumes (note, for example, the onset at 0.0) 4 (which is a subset of 1 forms, since the dotted line in 1 the same as the line in 4 ) shows the straight line equation. After DCS has been determined automatically (as mentioned above), the DAS can then use Eq. (1) empirically estimated. For the analyzed experimental data, the total error in estimating DAS from the DCS value is within 5%, which is statistically significant. For A356 and 319 (each containing 6 to 7% Si), the empirical equation is: DAS = 0.6334 × DCS - 8.4459 (DCS> 15 μm) (4) where R ^{2 is} a measure of the goodness of fit of a linear regression; in the version that in the 1 and 4 R ² - which is a dimensionless fraction between 0.0 and 1.0 - is 0.9516. By reference, an R ² value of 0.0 means that knowing a value along the abscissa (ie, the x-axis) does not help predict the corresponding value on the ordinate (ie, the y-axis). Under such circumstances, there is no linear relationship between the X and Y values, and the balance line is a horizontal line that can be oriented in any direction through the average of all Y values. If, on the other hand, R ^{2 is} equal to 1.0, then all the points lie exactly on a straight line without scattering, so that knowledge of the X value leads to an exact prediction of the Y value.

As mentioned above, is in a form, the difference between the DAS and DCS in relation to the volume fraction V _eu eutectic phases. The intersection method was developed by correlating measured data with a regression line equation. In the above-mentioned line cutting _method (DCS _li ), the DCS is equal to the value of the total length of the lines (for example, the circumference of five circles of the concentric circles 50 ) divided by the total number of intersections at the cell boundaries. Thus, the line-cut method is used to arrive at DCS values, which in turn are used to calculate on the basis of the above Eq. (1) or (2) to be converted into DAS values. As such, the line-slice method automatically detects DAS, regardless of whether the empirically-based approach or the physics-based approach is used. It can be seen that the one in Eq. (3) DCS value used does not take into account the width of the cell boundaries, with the volume fraction V _eu eutectic phases or regions 30 . 40 in relationship. In other words, the DCS method overestimates the Dendritzellengröße by only the volume fraction V _eu eutectic phases 30 . 40 as part of the dendrites 20 treated. The volume fraction V _eu eutectic phases, which regions the 30 . 40 may vary considerably depending on the alloy composition. In the aluminum alloys A356 (7% Si) and 319 (6% Si, 3.5% Cu), the volume fraction V _eu eutectic phase is about 50%. It will be appreciated that it is the black-and-white representation of the phases or regions indicated in the figure 30 and 40 difficult to identify and distinguish because they appear to be merged together (since both are shown in black); however, it will be appreciated by those skilled in the art that other representations (eg, colored ones) could be used to more clearly show the boundaries.

As mentioned above, the DAS can also be determined using the physically based eq. (2) be determined. For cast aluminum alloys of the theoretical volume fraction may be determined f _eu of eutectic phases from existing phase diagrams or thermodynamics computer software, as known in the art. The theoretical volume fraction f _eu eutectic phases is z. B. about 49% for A356 (7% Si, 0.4% Mg) and 43% for 319 (6% Si, 1% Fe, 0.5% Mn, 3.5% Cu).

In fact, the solidification of aluminum castings never proceeds under equilibrium conditions. Because of the limited diffusion of the dissolved substances in the solidified aluminum dendrites with the increase of the solidification rate of the actual volume fraction V _eu eutectic phases is usually greater than the theoretical value f _eu. For alloys with a high diffusibility of elements such as Si, the coefficient k varies from 1 to 1.1, while for alloys with elements having a low diffusivity, such as Cu, the coefficient k varies from 1 to 1.2. This is in good agreement with the experimental data presented in the 1 and 4 are shown.

Referring next to the 3A is an example of a typical alloy microstructure 110 to illustrate how the line-cut method is used to determine the size of the dendritic structure, such as the size of the dendritic structure. DCS and DAS according to the prior art. Referring next to the 3B and 3C approaches are shown that are used in the determination of DAS from captured images such. B. the of 3A be used. These figures are particularly in connection with the image analysis device 300 using image analysis software and other computerized facilities. As mentioned above, the image analyzer may be a machine (and / or software) to perform advanced image manipulation, enhancement, and analysis. In this approach, the length L2 is divided by the number of dendrites (in this case, five) to give the DAS. In one form, the DCS _ed parameter is defined as the average area equivalent diameter, while the Cáceres et al. Reference entitled Dendrite cell size , which was explained in Table 1 above, has used another parameter, the mean area-equivalent circle diameter of a dendrite cell, DCS _ed , measured by a semi-automatic technique, about the dendrites 120 define. In the present context, a semi-automatic approach is one in which portions of the analysis involve the use of manual steps (eg, in one or more of the middle steps). This roughly corresponds to the measuring method of Dendrite cell size of Jaquet and Hotz, which is also mentioned in Table 1. It is common in the 3A to use the method shown to manually measure DAS and DCS. With special reference to 3B is a Result of the analysis shown after all of the eutectic regions 30 . 40 of the 2A and 2C were reduced to a line representation. As such, all regions (represented by area A) are considered primary dendrite cells. The circle C has the same area as the representative region A.

With particular reference to the 2E and 3C in conjunction with the 3A and 3B is the area fraction of the eutectic regions in a detected point of interest of the image of 3A from the image analyzer 300 by converting the captured image to a eutectic image equivalent, wherein the ratio of dark regions (ie, the eutectic phases or regions 30 . 40 ) gives a measure of the surface area percentage of the eutectic with respect to the brighter regions. This in turn can be extrapolated to give the actual volume fraction V _eu . Likewise, the aspect ratio of the individual dendrite cells in the local region of interest in the image analyzer also becomes 300 measured after the dendrites have been processed to the in 3B to resemble the skeletal structure depicted, the method of obtaining the dendritic skeleton of Cáceres and Wang, "Dendrite Cell Size and Ductility of Al-Si-Mg Casting Alloys: Spear and Gardner Revisited", Int. J. Cast Metals Res., 1996, 9, 157-162 (which is incorporated herein by reference). From this, the aspect ratio of a particular dendrite cell A is determined by measuring the cell size along a well-defined direction; one way to do this is to divide the maximum Feret ("calipers") diameter F _max by the minimum Feret diameter F _{min of} the dendrite. In general, Feret diameters are particularly useful in the analysis of microscopic particles, cells, and similar images where projections of a three-dimensional (3D) object onto a 2D plane are used in such a way that the diameter is the distance between two parallel tangent lines instead of planes. Once all of this is detected, a relationship between the automatically measured DCS and the more desirable DAS can be calculated as follows: DAS = (1 - Veu) · DCS / √α (5) where alpha (a) is the average aspect ratio of the different cells in 3C are shown. In one sense, Eq. (5) as a more specific refinement of Eq. (2) by taking into account the square root of the average aspect ratio; such an approach provides greater accuracy, especially in cases where the dendrites are elongate. Such cases of high aspect ratios are predominant when DAS has small or medium values.

Referring next to 5 a flow chart according to an aspect of the present invention shows various steps 200 to automatically quantify DAS for a microsample of a material under study. As explained above, many of the steps 200 (or all) be automated (such as by suitable algorithms designed to allow a computer or similar processor driven device to act on them). Initially, a microsample of the material under investigation (not shown, but generally the one in 2A shown similar), 210 , In one form, the microprobe is first cut from a section in the casting of interest. The cut sample is then thermally attached with a resin to form a short cylinder having the area of interest of the cut sample on one end of the cylinder. After mounting, the specimen is wet sanded (eg, with sandpaper or the like) to expose the surface of the metal. Subsequently, the specimen is ground with a finer and a finer, abrasive medium. Once the information 220 (eg digital images) concerning the microsample, this information can be processed 230 , In cases where the information z. B. in the form of a scanned digital image, they can then in a manner similar to the above-mentioned 2C are processed. The line-cut method (explained above) 240 Can be used to analyze the image for DCS information 250 to create. Based on this, the DCS information becomes an image analysis 260 based on area or volume percentages. Thereafter, DAS can be calculated over one or the other of the above empirically based or theoretically based approaches, 270 , This information may be output to a user or to additional subsequent quantification programs, routines, algorithms, as well as to printing or storage devices for later use.

Referring next to 5A is a variant of in conjunction with 5 The only difference is that the DAS calculation step 270 from 5 through the more specific calculation step 270A from Eq. (5), with the above-described aspect ratio for the various dendrite cells within the point of interest of the image of FIG 3A is taken into account.

It should be understood that indications herein that a component of an embodiment is "designed" in a particular manner or that meets a particular characteristic or function in a particular manner are structural indications as opposed to indications of intended use. More specifically, references to the manner in which a component is "designed" herein refer to an existing physical state of the component and, as such, are to be understood as an unambiguous indication of the structural factors of the component. Likewise, to describe and define embodiments herein, the terms "substantially" and "approximately" are used herein to represent the natural level of uncertainty associated with any quantitative comparison, value, or value May be assigned to measurement or other representation, and as such may represent the degree to which a quantitative representation may deviate from a given reference, without resulting in a change in the basic function of the object under consideration.

Having described the embodiments of the present invention in detail and by reference to specific embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the embodiments defined in the appended claims. More specifically, although some aspects of embodiments of the present invention are referred to herein as preferred or particularly advantageous, it is contemplated that the embodiments of the present invention are not necessarily limited to these preferred aspects.

LIST OF REFERENCE NUMBERS

1)

Legierung 319

2)

Legierung 319

3)

Legierung 356

4)

Gemessener DAS + 1ơ

5)

Gemessener SDAS – 1ơ

Fig. 2E

1)

Legierung 319

2)

Legierung 319

3)

Legierung 356

4)

Gemessener DAS + 1SD

5)

Gemessener SDAS – 1SD

Fig. 5

210

Mikroprobenzubereitung

220

Digitale Bilder der Mikroprobe

230

Bildverarbeitung – Verschmelzen eutektischer Phasen

240

Verwenden des Linienschnittverfahrens mit konzentrischen Kreisen

250

Bildanalyse von DCS

260

Bildanalyse eutektischer Phasen (Flächen-% oder Vol.-%)

270

Berechnung von DAS – Gl. (1) oder Gl. (2)

Fig. 5A

210

Mikroprobenzubereitung

220

Digitale Bilder der Mikroprobe

230

Bildverarbeitung – Verschmelzen eutektischer Phasen

240

Verwenden des Linienschnittverfahrens mit konzentrischen Kreisen

250

Bildanalyse von DCS

260

Bildanalyse eutektischer Phasen (Flächen-% oder Vol.-%)

270

Berechnung von DAS – Gl. (5)

Fig. 2D

1)

Alloy 319

2)

Alloy 319

3)

Alloy 356

4)

Measured DAS + 1ơ

5)

Measured SDAS - 1ơ

Fig. 2E

1)

Alloy 319

2)

Alloy 319

3)

Alloy 356

4)

Measured DAS + 1SD

5)

Measured SDAS - 1SD

Fig. 5

210

Micro sample preparation

220

Digital images of the microprobe

230

Image processing - merging of eutectic phases

240

Using the concentric circle line cutting method

250

Image analysis of DCS

260

Image analysis of eutectic phases (area% or vol%)

270

Calculation of DAS - Eq. (1) or Eq. (2)

Fig. 5A

210

Micro sample preparation

220

Digital images of the microprobe

230

Image processing - merging of eutectic phases

240

Using the concentric circle line cutting method

250

Image analysis of DCS

260

Image analysis of eutectic phases (area% or vol%)

270

Calculation of DAS - Eq. (5)

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited non-patent literature

• Spear and Gardner (1963) [0005]
• Jaquet and Hotz (1992) [0005]
• Levy et al. (1969) [0005]
• Oswalt and Misra (1980) [0005]
• Radhakrishna et al. (1980) [0005]
• Flemings et al. (1991) [0005]
• McLellan (1982) [0006]
• Levy et al. (1969) [0006]
• ASTM E3 [0007]
• ASTM E407 [0008]
• ASTM E3 [0010]
• ASTM E3 [0013]
• Cáceres et al. Reference entitled Dendrite cell size [0038]
• Cáceres and Wang, "Dendrite Cell Size and Ductility of Al-Si-Mg Casting Alloys: Spear and Gardner Revisited", Int. J. Cast Metals Res., 1996, 9, 157-162 [0039]

Claims (10)

A method of automatically quantifying a dendrite arm spacing in a hypoeutectic cast aluminum part, the method comprising: a computerized system is used to capture an image corresponding to a point of interest in the casting; the computerized system is used to estimate a dendrice cell size within the point of interest by superimposing the image on a grid pattern to quantify the number of interfaces between at least one cell boundary of the image and at least a portion of the grid pattern; the computerized system is used to estimate a volume fraction of eutectic phases in the site of interest; the computerized system is used to estimate an aspect ratio of at least one dendrite cell within the point of interest; and the computerized system is used to convert the estimated dendrice cell size, the estimated volume fraction and the estimated aspect ratio into a quantified dendrite arm spacing. The method of claim 1, wherein the computerized system comprises an image analyzer to perform the detection and at least part of the determining. The method of claim 1, wherein the grid pattern comprises a series of linearly spaced lines, the series of linearly spaced lines comprising a series of concentric circles, the dendrice cell size being estimated by the formula DCS = L / n where L corresponds to a total perimeter of the series of concentric circles and n corresponds to the total number of intersections between the circular lines and the at least one cell boundary. The method of claim 1, wherein converting the estimated dendrice cell size, the estimated volume fraction, and the estimated average aspect ratio to a quantified dendrite arm spacing is expressed by: DAS = (1 - V _eu ) · DCS√ (α) where V _eu defines the volume fraction and α is the aspect ratio. The method of claim 1, wherein the aspect ratio is defined by the ratio of the maximum linear dimension of the dendrite cell to the minimum linear dimension of the dendrite cell. The method of claim 1, wherein using the computerized system to estimate a volume fraction of eutectic phases in the site of interest comprises transforming the captured image into a eutectic image equivalent. A method of automatically quantifying a dendrite arm spacing in a hypoeutectic cast aluminum part, the method comprising: a cast material to be analyzed is selected; an image analyzer is used to automatically determine dendrite cell size information corresponding to a point of interest in the selected casting material; and the dendrice cell size information is converted into dendrite arm distance information. The method of claim 7, wherein using an image analyzer to automatically determine dendrite cell size information comprises: an image is captured that corresponds to the point of interest; and estimating a dendrice cell size within the point of interest by superposing the image on a grid pattern to quantify the number of interfaces between at least one cell boundary of the image and at least a portion of the grid pattern. An article of manufacture comprising a computer-usable medium having computer-readable program code therein for automatically quantifying a dendrite arm spacing for a hypoeutectic cast aluminum material, the computer-readable program code in the article of manufacture comprising: a computer-readable program code portion for causing the computer to collect data relating to digital information of a point of interest a sample of the casting material assumes; a computer readable program code portion for causing the computer to process the digital information into dendrice cell size information; a computer readable program code portion for causing the computer to convert the dendrite cell size information to a corresponding dendrite arm spacing via an algorithm based on a volume fraction of eutectic phases in the point of interest and an aspect ratio of at least one dendrite cell within the point of interest; and a computer readable program code portion for causing the computer to generate an output corresponding to the dendrite arm spacing. The article of manufacture of claim 9, wherein the computer readable program code portion for causing the computer to convert the dendrite cell size information to a corresponding dendrite arm spacing is expressed by: DAS = (1 - V _eu ) · DCS / √ (α) where V _eu defines the volume fraction of eutectic phases in the site of interest and α defines the aspect ratio of at least one dendrite site within the site of interest.

Images