JPS59166863A - Fracture analyzing method of cast iron - Google Patents

Abstract

PURPOSE:To decide quickly a material strength of a cast iron by substituting an area ratio occupied by a black part derived from a white or black binary picture by setting a prescribed luminance level as a threshold level, as to a picture signal fetched by reading and scanning a fracture of a cast iron, into a relational expression to a tensile strength set in advance. CONSTITUTION:A picture signal read by a video camera 2 is inputted to an A/D converting part 11 of a picture processing unit 10. The signal fetched to the A/D converting part 11 is divided into picture elements of 256X256, and the divided signal concerned is hexadecimal-coded to 16 luminance levels extending from snow-white to coal-black in accordance with variable density of white and black. The hexadecimal-coded picture signal sets a prescribed luminance level as a threshold level, and is converted to a black or white binary signal. By the binary-coded picture signal, the storage contents of a memory 13 are rewritten to a signal corresponding to a binary picture. A CPU22 of a microcomputer 20 calculates a ratio to the whole fracture area of a sample 1, for instance, of a black part, basing on said picture signal binary-coded to black or white.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a fracture surface analysis method for cast iron, which is a material for casting, so that the material strength of the cast iron can be quickly determined.

Cast iron includes gray cast iron in which flaky graphite is precipitated, spheroidal graphite cast iron in which spheroidal graphite is precipitated, and an intermediate type between these gray cast irons and spheroidal graphite cast iron, in which graphite is precipitated in the form of incomplete spheres, corals, or fibers. There are intermediate-type graphite cast irons (the definition of structure, material, etc. is still unclear, so in this specification, it is referred to as intermediate-type graphite cast iron), and fracture surface analysis methods are usually adopted to determine the material quality of these cast irons. .

As is well known, fracture surface analysis is a method in which a chisel is inserted near one end of the material and the material is broken with a hammer, etc., and the material, structure, and internal defects can be determined from the appearance of the fracture surface. In the past, the quality of the material was determined by manually examining the material layer of the sample and observing the structure using a microscope. Of course, this makes it impossible to quickly determine materials for a large number of products, and it is difficult to say that objective and unified determinations are being made.

This invention has been made in view of the above-mentioned circumstances, and provides a fracture surface analysis method for cast iron that can quickly determine the tensile strength of cast iron materials based on systematic judgment. The purpose is to

That is, this invention focuses on the strong correlation between the ratio of the black part of the fracture surface of a cast material to the total fracture surface area and the tensile strength of the cast material, and based on this correlation, the tensile strength of cast iron is Specifically, the broken surface of the cast iron is read and scanned by a video camera or the like, extracted as an image signal, and this image signal is set at a predetermined brightness level as a threshold. After creating a white or black binary image, calculate the area ratio occupied by, for example, a point part in the binary image using a microcomputer or the like,
Furthermore, the tensile strength of the cast iron is derived by substituting, for example, the calculated area ratio into the preset correlation equation.

Hereinafter, the fracture surface analysis method for cast iron according to the present invention will be described as an example shown in the attached drawings. - will be explained in detail.

In this example, the above-mentioned intermediate type graphite cast iron was used as a sample for material determination. Intermediate graphite cast iron has recently attracted attention because it has better castability and better thermal conductivity than fully spherical graphite cast iron, but from a manufacturing standpoint, it is easier to make it completely spherical and it is difficult to make the entire casting. It is rather difficult to make all intermediate type graphite, and requires considerable effort in manufacturing, so determining the quality of the material after manufacturing is extremely important in ensuring stable quality.

Figure 1 shows an enlarged photograph of the fracture surface of intermediate graphite cast iron. In FIG. 1, the black parts of each fracture surface have a ferrite composition, and the white parts have a pearlite composition. Generally, a ferrite structure has a lower tensile strength than a pearlite structure. Therefore,
It was found that there was a strong correlation (correlation coefficient of 0.82 or more) between the entire fracture surface of the portions identified as black using a predetermined threshold.

Figure 2 approximates the above correlation to a linear relationship, and according to Figure 2, the black fracture surface ratio and tensile strength of each intermediate type graphite cast iron shown in Figures 1 (a) to (e) K. The relationship with strength is shown in the table below.

Next, FIG. 3 shows a specific configuration example for carrying out the cast iron fracture surface analysis method according to the present invention.

In Fig. 3, l is a sample of intermediate graphite cast iron with a diameter of about 10 to 30, 2 is a video camera, 3 is a first monitor, 4 is a second monitor, and 10 is an analog-digital (A/D)
) An image processing unit 20 comprising a conversion section 11, an input/output section 12, and a memo IJ 13 is an input/output section 21XOP.
This is a so-called microcomputer that includes a U22, a memory 23, a printer 24, and the like.

The image processing unit IO is a unit that connects the video camera 2 and the microcomputer 20 to perform image analysis and image processing, and captures the video and stores the processing by the microcomputer 20 as digital data in line 7. . Digitally converted image data is
One screen has 256 x 256 pixels, each of which is stored at 16 brightness levels (4 bits).

The microcomputer 20 converts the digitally converted image data into the 16 brightness levels into a black or white binary image using an appropriate brightness level as a threshold, and calculates the black fracture rate in the binary image. Then, by substituting the calculation results into the relational expression between the tensile strength and black fracture surface ratio of intermediate graphite cast iron, which was set in advance based on the measurement results obtained from a large number of samples, the intermediate graphite cast iron can be estimated. Derive the strength.

Next, the operation of the embodiment shown in FIG. 3 will be explained with reference to FIG. 4 and the like. Note that (a) in Figure 4 shows a simple example of the fracture surface of intermediate graphite cast iron, in which the diagonal broken part shows dot-colored ferrite fibers, and the other parts show white pearlite fibers. It shows the braided fibers. Further, the horizontal axes of the graphs shown in FIGS. 4(b) to 4(d) correspond to the running line X in FIG. 4(a).

First, a sample IK eyeglass with a diameter of 10 to 30+mn taken from a ladle is placed and broken with a hammer or the like. Then, this sample 1 is placed on a sample stage, and positioned directly below the lens of the video camera 1 using a suitable jig. Next, the video camera 2 reads the fracture surface image of the sample 1, and the read image is input to the first monitor 3. While viewing the image on the first monitor 3, the operator adjusts the aperture, focus, etc. of the video camera 2. At this time, the image projected on the first monitor 3 is an analog image as shown in FIG. 1, and the brightness distribution of the output of the camera 2 is as shown in FIG. 4(b).

Thereafter, the image signal read by the video camera 2 is input to the A/D converter 11 of the image processing unit 1 (1). A
/ The signal taken into the D converter ll is 256X 25
It is divided into 6 pixels, and the divided signal is shown in Fig. 4(C).
As shown in the figure, the image is 16-valued into 16 brightness levels from pure white (brightness 15) to pure black (brightness 0) depending on the shading of black and white. One fixed number divided into 16 levels is input/output section 12
The information is then input to the memory 13 and stored in the memory 13. In addition, this 16-valued image signal is transmitted to the input/output section 1.
2 to the second monitor 4, and is displayed on the screen of the second monitor 4.

Next, the 16-valued image signal stored in the memo IJ 13K is sent to the microcomputer 20+7) input/output section 2.
1 to the 0PU22. The image signal input to the CPU 22 is processed in the memory 23 (according to a program stored in advance).

In this process, first, the 16-valued image signal is set to a predetermined brightness level, for example, a threshold value of brightness 7, and parts lower than the threshold value are pure black (brightness 0), and parts higher than the threshold value are black. Or the equal part is pure white (brightness 15)
It is converted into a binary code of black or white. Fourth
- Figure (d) is an example in which the luminance I level in Figure 4 (C) is adjusted to 211t, and Figure 4 (e) is an example in which the luminance I level in Figure 4 (C) is changed to 211t.
This is an example of binarization using the brightness level of Q) as a threshold value. In this way, the binarized image signal is input to the memory 13 via the input/output units 21 and 12, and the stored contents of the memory 13 are rewritten into a signal corresponding to the binary image. This binarized image signal is then sent to the input/output section 12.
is input to the second monitor 4 again via the second monitor 4.
0 screen (refer to reference figure ugliness: (a) to (8) in this reference figure are the same as (a) to (θ
).

Along with this, 0PU2 of microcomputer 2°
2 calculates, for example, the ratio of the black portion of the sample 1 to the total fracture surface area (black fracture surface ratio) based on the image signal binarized into black or white. The program stored in the memory 23 includes a program related to the relational expression (see Fig. 2) between the tensile strength of intermediate graphite cast iron and the above-mentioned black fracture surface ratio, which was set based on the measurement results of a large number of samples as described above. is also included. The cptr 22 derives the estimated tensile strength of the sample l based on this program and the black fracture ratio of the sample l calculated above, and calculates the estimated tensile strength of the sample l.
Output to printer 24. The operator can immediately know the estimated strength of the sample 1 by simply looking at the output of the printer 24.

In addition, in the field of image processing, is J4 degree level? 2nd level, 4th
It can be divided into 8 levels, 16 levels, 32 levels, 64 levels, 128 levels, 256 levels, and 512 levels, but the influence of external t7μ light, cost,
Considering aspects such as processing speed, it is considered that a level 16 or higher is unnecessary.

Of course, since the image signal will ultimately be binary (number N), it is of course possible to employ a two-level image processing unit with a predetermined brightness level as the threshold value. .

Furthermore, although medium 1iJ] type graphite cast iron was used as the sample in this example, it goes without saying that the present invention can also be applied to determining the tensile strength of other spheroidal graphite cast irons or gray cast irons.

Furthermore, the present invention is particularly effective when incorporated into the material testing process of the casting manufacturing process. For example, if the casting material to be tested is sequentially passed directly under a video camera, and the material strength of the passed material is automatically output from the printer, the tester can simply look at the content output from the printer. can easily identify defective materials.

As explained above, according to the cast iron fracture surface analysis method according to the present invention, (1) the tensile strength of cast iron can be quickly determined based on systematic judgment.

(2) It can be an effective method that can be adopted for determining material properties for a large number of sample pressures.

and other excellent effects.

[Brief explanation of drawings]

Figure 1 is a photograph showing the fracture surface of intermediate graphite cast iron, Figure 2 is a graph showing the relationship between the tensile strength and black fracture rate of intermediate graphite cast iron, and Figure 3 is a fracture surface analysis of the cast iron according to the present invention. FIG. 4 is a block diagram showing one embodiment of the method, and is an explanatory diagram for explaining the operation of the embodiment shown in FIG. 3. l...Sample, 2...Video camera, 3...First monitor, 4...Second monitor, 10...Image processing unit/), 11...A/D conversion section, 12 .21...Input/output section, 13.23...Memo'J, 20...Microcomputer, 22...CPU, 24...Printer Fig. 4

Claims (1)

[Claims] In a cast iron fracture surface analysis method that derives the tensile strength of the cast iron by analyzing the cast iron fracture surface, the fracture surface of the cast iron is read and scanned and extracted as an image signal, and the extracted image signal is set at a predetermined brightness level. After creating a white or black binary image using the threshold value, calculate the area ratio occupied by the white part or point part in the binary image, and then calculate the calculated area ratio as the preset tensile strength of cast iron. A fracture surface analysis method for cast iron, characterized in that the tensile strength of the cast iron is derived by relating the correlation between area ratios.