TWI489396B - Image structure analysis method - Google Patents

Description

Image structure analysis method

The invention relates to an image structure analysis method, and in particular to a structure analysis method for outputting a digital spheroidization rate by a full-automatic image scanning method.

At present, the detection of general spheroidization rate can be divided into metallographic image comparison and digital image recognition detection. Metallographic image comparison is mainly through the use of microscopic filming, through artificial operation or semi-automatic way to compare metallographic images, the disadvantage is that relying on human judgment lacks objective conditions, and the other is semi-automatic, although the machine is utilized. Judging the spheroidization rate, but still need to manually set the parameters, there is still a problem of operational negligence in the judgment of the spheroidization rate in the metallographic map.

The invention provides an image structure analysis method, which analyzes the spheroidization rate by using an image structure image, thereby improving the convenience of obtaining the spheroidization rate of the structure of the object to be tested.

The invention provides an image structure analysis method, which comprises: obtaining a structural test piece by using an image capturing device, wherein the structural test piece has a plurality of particles. Having an arithmetic processing unit perform a normalization operation according to the structure test piece map, and use a constant cross-tab to compare one of the particle effects of each of the particles to obtain an optimal signal-to-noise ratio of each of the particles, according to the best information The noise ratio is plotted against a factorial effect to obtain an optimal parameter and an optimal spheroidization rate is output based on the optimal parameters.

In an embodiment of the invention, the performing a normalization operation further comprises causing the spheroidization rate of the particles to be between 0 and 1 unit.

In an embodiment of the present invention, the step of using a cross-tab to compare a factor effect of each particle to obtain an optimal signal-to-noise ratio of each of the particles is further Including: using the one-field analysis method to obtain several orthogonal factors of influence factors, according to the influence of the influence factor on one of the particles, according to the influence effect to obtain the best signal-to-noise ratio of each particle. Furthermore, in the step of using the one-field analysis method to obtain the consistent cross-tabulation of several influencing factors, the Taguchi analysis method uses a one-factor method to list the orthogonal table.

In an embodiment of the present invention, the image structure analysis method further comprises: converting the structural sample image into a gray-scale correlation coefficient, and integrating the gray-scale correlation coefficient to generate a gray-scale correlation degree.

The invention further provides an image structure analysis method according to the gray scale coefficient, comprising: obtaining a structural test piece by using an image capturing device, wherein the structural test piece has a plurality of particles. An arithmetic processing unit performs a normalization operation according to the structure test piece map, converts the structural test piece image into a gray-scale correlation coefficient, and integrates the gray-scale correlation coefficient to generate a gray-scale correlation degree. Using a constant cross-tab to compare the factor effects of each of the particles to obtain an optimal signal-to-noise ratio of each of the particles, and plot a factor-effect map according to the optimal signal-to-noise ratio to obtain an optimal parameter. And, an optimal spheroidization rate is output according to the optimal parameters.

S110~S160‧‧‧Step flow chart

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a flow chart showing the steps of a preferred embodiment of the present invention.

2 to 3 are flow chart diagrams of the steps in Fig. 1.

4 to 6 are metallographic views of an embodiment of the present invention.

The above described features and advantages of the present invention will be more apparent from the following description.

Please refer to FIG. 1, FIG. 2 and FIG. 3 together. FIG. 1 is a flow chart of the steps of the preferred embodiment of the present invention. 2A-2B are flow chart diagrams of the steps in Fig. 1. In FIG. 1, the image structure analysis method includes: obtaining a structural test piece map by using an image capturing device, the structural test piece having a plurality of particles (step S110). In this step, it is preferable to obtain a structural test piece by a microscope combined with an image capturing device.

Then, an arithmetic processing unit performs a normalization operation according to the structure test piece map (step S120). In this step, the purpose of the normalization is to reduce the spheroidization rate of the particles to 0~1 unit. between. For example, a metallographic diagram of a structure that is magnified 500 times or more by a microscope is taken, and the metallographic map is sent to a computer for identification, and 100 pellets are manually selected for screening, and the pellets having an aspect ratio of less than 5 are passed. If the aspect ratio is greater than 5, the equation is not as follows, so the spheroidization rate SR is determined as follows: SR = 100 - the number of particles not passed × 100%

If the program is digitally detected, there are two calculation methods. One is the granular calculation method, which is a proportional formula for simply calculating the number of carbide particles in accordance with the aspect ratio. The calculation formula is as follows:

In addition, the spheroidization rate is calculated by the area calculation method. This method is mainly to calculate the area of carbide particles that meet the aspect ratio conditions. The calculation formula is as follows:

Using a constant cross-tab to compare one of the particle spheroidization rate calculations of each of the particles to obtain an optimum signal-to-noise ratio of each of the particle spheroidization ratios (step S130), and in this step, further reference is made to the figure. Step 2A. First, an iterative analysis method is used to obtain an orthogonal table of several influence factors (step S131), and an influence factor affects one of the particles according to the orthogonal table (step S132), and finally, each particle is obtained according to the influence effect. One of the best signal to noise ratios (step S133).

Furthermore, in the present invention, the Taguchi analysis method mainly adopts a one-factor method, in which only one factor is changed at a time, and other factors are maintained at the level of the previous experiment, and the orthogonal test factor In this method, the experimental factor is an even distribution of the orthogonality to explore the effect of the level change of the factor. Taguchi The implementation steps of the analytical method can be classified into at least the following: 1. selected quality characteristics; 2. ideal function for determining quality characteristics; 3. lists all factors affecting the quality characteristics; 4. sets the level of control factors; Determine the level of the interference factor, if necessary, conduct interference experiments; 6. Select the appropriate orthogonal table; 7. Execute and record the data; 8. Data analysis; 9. Confirm the results.

Performing a binarization calculation according to the optimal signal-to-noise ratio to obtain an optimal parameter (step S140). In this step, the method further includes: calculating an initial spheroidization rate of the particles (step S141), and comparing each The initial spheroidization rate of the particles is obtained to obtain an initial spheroidization rate orthogonal table (step S142), and then, an optimum parameter is obtained based on the initial spheroidization rate orthogonal table (step S143). Finally, an optimal spheroidization rate is output according to the optimal parameter (step S150), thereby optimizing the spheroidization rate and comparing the best spheroidization rate in the orthogonal table experiment, which is the best, and the final optimal spheroidization rate. (Step S160).

In this embodiment, the image structure analysis method further comprises: converting the structural test piece image into a gray-scale correlation coefficient, and integrating the gray-scale correlation coefficient to generate a gray-scale correlation degree.

Please refer to FIG. 4 to FIG. 6 , which are metallographic diagrams of an embodiment of the present invention, wherein FIG. 4 is a metallographic structure diagram of a low carbon steel taken by a 1,500-fold electron microscope, and FIG. 5 is a low alloy in a 2,500-fold electron microscope. The metallographic structure of carbon steel, Figure 6 is a metallographic structure of a low-alloy medium carbon steel taken with a 1,000-fold electron microscope. The metallographic diagrams of Figures 4 to 6 can be applied to the present invention to optimize the spheroidization rate.

While the present invention has been described above in the foregoing embodiments, it is not intended to limit the invention, and the equivalents of the modifications and retouchings are still in the present invention without departing from the spirit and scope of the invention. Within the scope of patent protection.

S110~S160‧‧‧Step procedure

Claims (10)

An image structure analysis method includes: obtaining a structural test piece by using an image capturing device, wherein the structural test piece has a plurality of particles; and causing an arithmetic processing unit to perform a normalized operation according to the structural test piece; Always comparing the factor effects of the particles affecting the image to obtain the best signal-to-noise ratio of each of the particles; performing a binarization calculation according to the optimal signal-to-noise ratio to obtain one of the most Good parameters; and output an optimal spheroidization rate according to the optimal parameter; and optimize the spheroidization rate to compare with the best spheroidization rate in the orthogonal test, and the best is the final best spheroidization rate. The image structure analysis method according to claim 1, wherein the step of obtaining a structural test piece by using an image capturing device further comprises: obtaining the structural test piece by using a microscope. The image structure analysis method according to claim 1, wherein the step of performing a normalization operation further comprises: making the spheroidization rate of the particles between 0 and 1 unit. The image structure analysis method according to claim 1, wherein the step of comparing the factor effects of each of the particles to obtain an optimum signal-to-noise ratio of each of the particles is further The method comprises: using a Taguchi analysis method to obtain the orthogonal table of the plurality of influence factors; and affecting the effects of the influence factors on one of the particles according to the orthogonal table; and obtaining the particles according to the influence effect One of the best signal to noise ratios. For example, in the image structure analysis method described in claim 4, wherein the Taguchi analysis method is used to obtain a plurality of influencing factors, the Taguchi analysis method adopts the orthogonal distribution factor method. The out of the table. For example, the image structure analysis method described in claim 1 of the patent application includes: Converting the structural test piece map into a gray scale correlation coefficient; and integrating the gray scale correlation coefficient to generate a gray scale correlation degree. An image structure analysis method includes: obtaining a structural test piece by using an image capturing device, wherein the structural test piece has a plurality of particles; and causing an arithmetic processing unit to perform a normalization operation according to the structural test piece; The structural test piece map is converted into a gray-scale correlation coefficient, and the gray-scale correlation coefficient is integrated to generate a gray-scale correlation degree; and a factor effect of each of the particles is compared by using a constant cross-tab to obtain each of the particles One of the best signal-to-noise ratios; performing a binarization calculation based on the optimal signal-to-noise ratio to obtain an optimal parameter; and outputting an optimal spheroidization rate according to the optimal parameter; and optimizing the ball The rate of spheroidization is compared with the best spheroidization rate in the orthogonal test. The best spheroidization rate is the best. The image structure analysis method according to claim 7, wherein the step of comparing the factor effects of each of the particles to obtain an optimum signal-to-noise ratio of each of the particles is further The method comprises: using a Taguchi analysis method to obtain the orthogonal table of the plurality of influence factors; according to the orthogonal table, the influence factors affect the one of the particles; and obtaining the particles according to the influence effect An optimal signal to noise ratio. For example, in the image structure analysis method described in claim 8, wherein the Taguchi analysis method is used in the step of obtaining a plurality of influence factors, the Taguchi analysis method adopts the orthogonal distribution factor method. The out of the table. The image structure analysis method according to claim 7, wherein the step of performing a binarization calculation according to the optimal signal-to-noise ratio to obtain an optimal parameter further comprises: calculating one of the particles Initial spheroidization rate; Comparing the initial spheroidization ratio of each of the particles to obtain an initial spheroidization rate orthogonal table; and obtaining the optimal parameter according to the initial spheroidization rate orthogonal table.