JP2909756B2 - Nonmetallic inclusion inspection method and nonmetallic inclusion inspection apparatus - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Field of Industrial Application) The present invention relates to a nonmetallic inclusion inspection method and a nonmetallic inclusion inspection apparatus using an image processing technique.

(Prior Art) Since non-metallic inclusions existing in a metal material affect the mechanical properties of the metal material, it is very important to perform a quantitative analysis of these in performing quality control of the metal material.

Conventionally, ASTM (American Society for Testing Mat) has been used as a method for inspecting nonmetallic inclusions present in metallic materials.
A method called the "erials" method is known. In this method, non-metallic inclusions appearing on the surface of a sample collected from a metal material are observed with a microscope, and the quality of the metal material is determined by its shape and distribution. According to the ASTM method, non-metallic inclusions are classified into the A type as shown in FIG.
It is classified into six types: B type, C type, D type, TiB type, and TiD type. Furthermore, each is classified into Thin (thin type) and Heavy (thick type). Then, the quality of the metal material is determined for each of the classified non-metallic inclusions.

By the way, in the visual inspection by an inspector with the naked eye, there are problems in terms of inspection speed and errors, and recently, an inspection apparatus for automatically inspecting a metal material using an image processing technique has been developed.

 The configuration of this inspection apparatus will be described with reference to FIG.

A sample 1 collected from a metal material is stored in a holder 2 and fixed to a stage of a microscope 3. The sample 1 fixed to the stage is illuminated by the illumination light source 4 coaxially with the optical axis of the microscope 3. The image of the sample 1 magnified by the microscope 3 is displayed on an ITV camera (Ind
ustrial Television (industrial television camera) 5 converts the video signal into a video signal. The video signal output from the ITV camera 5 is input to the image processing device 6 and is subjected to predetermined image processing.

The image processing device 6 includes an A / D converter 7, a frame memory 8, and a control CPU 9, and an input video signal is first converted into a density for each pixel constituting one screen by the A / D converter 7. Are converted into, for example, 8-bit digital density information, and are sequentially stored in the frame memory 8.

Next, the density information stored in the frame memory 8 is
The control CPU 9 binarizes the data with a predetermined threshold. This is because the density information stored in the frame memory 8 has 8-bit width, and it is difficult to grasp the shape of the non-metallic inclusion as it is. Therefore, attention is paid to the fact that the density of the non-metallic inclusion is substantially different from that of the background of the image, and the density information is binarized using an appropriate density as a threshold value, whereby the shape of the non-metallic inclusion is clearly extracted.

Then, the density information is transmitted to the host via the control CPU 9.
The data is sent to the CPU 10 and analyzed based on characteristics such as the shape and concentration of the nonmetallic inclusion.

Reference numeral 11 denotes an image monitor that displays an image of the sample 1 on which image processing has been performed.

In the inspection of non-metallic inclusions using such an image processing technique by an inspection device, the image quality of a binarized image greatly affects the inspection density. On the other hand, since the brightness of the background of the original image and the contrast with non-metallic inclusions differ depending on the type of metal material and the sample, it is not possible to set the threshold value fixedly. Must be set selectively.

Therefore, conventionally, the inspector repeatedly sets the threshold value which seems to be optimal by repeatedly performing the trial on the image of the image-processed sample displayed on the image monitor 11.

However, it takes time to repeat trials until an optimum threshold is determined, and errors due to the inspector cannot be avoided.

(Problems to be Solved by the Invention) As described above, in the conventional inspection apparatus for non-metallic inclusions, since the threshold value for binarizing the concentration information is set by the trial of the inspector, it takes time for the inspection. In addition, there is a problem that the inspection accuracy is poor.

The present invention has been made in view of such a point,
An object of the present invention is to provide an inspection apparatus for nonmetallic inclusions, which can improve the inspection efficiency by automating the setting of a threshold value and perform an inspection with high accuracy.

[Constitution of the Invention] (Means for Solving the Problems) The nonmetallic inclusion inspection method of the present invention includes a step of enlarging a metal sample containing nonmetallic inclusions with a microscope, and a step of: Converting the image signal into a video signal, multiplying the density of each pixel constituting the video signal, storing density information of each of the multi-valued pixels, Detecting a background concentration peak value of the non-metallic inclusion on the metal sample surface using concentration information, and subtracting a value preset for each type of the metal sample from the background concentration peak value. By setting a threshold for identifying the non-metallic inclusions contained in the metal sample and the background of the metal sample, binarizing the stored concentration information based on the threshold, at least the non-metal Of inclusions It is characterized by comprising the step of extracting Jo.

In addition, the non-metallic inclusion inspection apparatus of the present invention includes a microscope that magnifies a metal sample including a non-metallic inclusion, an imaging unit that converts a metal sample surface magnified by the microscope into a video signal, A converter for converting the density of each pixel constituting a signal into multi-valued data; storage means for storing density information of each pixel multi-valued by the converter; and the metal sample using the density information of each pixel. A background concentration peak value detecting means for detecting a background concentration peak value of the non-metallic inclusions on the surface; and a background concentration peak value detected by the background concentration peak value detecting means, which is preset for each type of the metal sample from the background concentration peak value. By subtracting the value, threshold setting means for setting a threshold for identifying the non-metallic inclusions contained in the metal sample and the background of the metal sample, and the stored concentration information based on the threshold And digitizing is characterized by comprising a non-metallic inclusion analysis means for extracting a shape of at least the non-metallic inclusions.

The threshold is set between a peak value and a minimum value.

(Operation) In the present invention, the density is multi-valued for each pixel of the video signal of the sample viewed in a magnified manner, and the number of pixels for each density is totaled based on the density information of each multi-valued pixel. Create a density histogram. Then, the density having the largest number of pixels is detected as the peak value of the density. Since the peak value of this concentration becomes the peak value of the concentration of the background of the non-metallic inclusion, the concentration information stored in the storage means is further converted into a binary value by subtracting a predetermined value from the peak value of this concentration. Become As a value to be subtracted from the peak value, for example, a value predetermined for each type of metal material is used.

Therefore, an automated threshold can be set without depending on the judgment of the operator, and the inspection can be performed at high speed and with high accuracy.

(Example) Hereinafter, an example of the present invention is described using a drawing.

FIG. 1 is a diagram showing a configuration of a nonmetallic inclusion inspection apparatus according to one embodiment of the present invention.

As shown in the figure, the inspection apparatus includes an optical system 200 for obtaining a video signal obtained by enlarging a sample 100 collected from a metal material, and a video signal obtained by the optical system 200 for image processing. And a processing system 300 for analyzing metal inclusions.

The optical system 200 includes a holder 210 for accommodating a plurality of samples 100, an optical microscope 220 for optically enlarging the sample surface, and an ITV camera for imaging the sample surface enlarged by the optical microscope 220 and converting the image surface into a video signal. (Industrial Television) 230, an illumination light source 240 that illuminates the sample surface coaxially with the optical axis of the microscope 220, an autofocus controller 250 that controls an autofocus mechanism provided in the microscope 220, and positions of the holder 210. XY stage 260 for moving X in the X-Y2 direction, and X based on an instruction from the processing system 300.
The XY stage controller 270 controls the movement of the Y stage 260.

The processing system 300 controls the entire inspection apparatus and processes the measurement data. The information processing apparatus 310 inputs a video signal from the ITV camera 230 based on an instruction from the information processing apparatus 310 and performs image processing necessary for the inspection. Image processing device 320, ITV camera 230
Image monitor 330 for displaying a raw image captured by the image processing device, image monitor 340 for displaying an image processed by the image processing device 320, and a video printer for outputting a hard copy of the image processed by the image processing device 320 350, a printer 360 that prints measurement data and the like processed by the information processing device 310.

FIG. 2 shows a detailed configuration of a portion related to image processing of the image processing device 320.

In the figure, reference numeral 321 denotes a control CPU that controls the entire image processing apparatus 320.

Reference numeral 322 denotes an A / D converter for converting the density of a video signal input from the ITV camera 230 into a digital value, and reference numeral 323 denotes an A / D converter.
This is a frame memory for storing the density information converted by the D converter 322.

Further, reference numeral 324 denotes an image processing dedicated processor for performing image processing of image data composed of density information stored in the frame memory 323, and reference numeral 325 denotes a work RAM used by the image processing dedicated processor for image processing.

Next, the operation of image processing in the image processing apparatus 320 of this embodiment will be described with reference to the flowchart shown in FIG.

First, an image of the sample 100 is taken into the frame memory 323 (Step 301). This means that the density of each pixel of, for example, 512 pixels vertically × 512 pixels horizontally constituting a video signal for one screen input to the A / D converter 322 is, for example, 8 bits (0 to 255) by the A / D converter 322. This is performed by sequentially storing the density information in the frame memory 323.

Next, a density array corresponding to the densities 0 to 255 is provided on the work RAM, and an initial value 0 is set for each of them (step S1).
302). Then, density information for one pixel is read from the frame memory 323 (step 303), and the value of the density array corresponding to the density information is incremented by 1 (step 304). After this,
It is checked whether reading has been performed for all pixels (step 30).
5) Steps 303 to 305 are repeated until it is determined that all the pixels have been read. And
When it is determined that all the pixels have been read, a density histogram indicating the distribution of the number of pixels for each density is created in the density array. FIG. 4 shows a graph of this density histogram.

Further, a density peak value G is obtained from the density array (step 306). As can be easily understood from the density histogram, the density peak value G is the density corresponding to the largest value in the density array.

Thereafter, a threshold value TH is obtained by subtracting a predetermined value C for each metal material from the peak concentration value G (step 30).
7) The density information in the frame memory 323 is binarized using this threshold value (step 308).

Therefore, since the binarization threshold is obtained by calculation from the density information of the captured image of the sample, the setting of the threshold can be automated.

Note that the number of pixels constituting one screen, the resolution of density, and the like are not limited to the present embodiment, but may be other values.

[Effects of the Invention] In the present invention, the concentration peak value is calculated from the concentration information of the sample taken into the storage means, and the binarization threshold value is further calculated from the concentration peak value. Inspection can be performed at high speed and with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing a configuration of an inspection apparatus for non-metallic inclusions according to an embodiment of the present invention, and FIG. 2 is a part relating to image processing performed by an image processing apparatus of the inspection apparatus. FIG. 3 is a flowchart showing the image processing operation of this image processing apparatus, FIG. 4 is a density histogram created from density information, and FIG. 5 is a classification of nonmetallic inclusions by the ASTM method. FIG. 6 is a block diagram showing the configuration of a conventional non-metallic inclusion inspection apparatus. 100 ... sample, 220 ... optical microscope, 230 ... ITV camera,
240 illumination light source, 260 XY stage, 310 information processing device, 320 image processing device, 321 control CPU, 3
22 A / D converter, 323 Frame memory, 324
… Processor for image processing, 325 …… Work RAM, 330, 340
…… Image monitor.

──────────────────────────────────────────────────続 き Continued on front page (58) Field surveyed (Int.Cl. ⁶ , DB name) G01N 21/84-21/90

Claims (2)

(57) [Claims] 1. A step of enlarging a metal sample including non-metallic inclusions with a microscope, a step of converting the magnified metal sample surface into a video signal by imaging means, and a step of converting each pixel constituting the video signal Multiplying the density; storing the multivalued density information of each pixel; and using the density information of each pixel, the background density peak value of the non-metallic inclusion on the metal sample surface. And detecting the non-metallic inclusions contained in the metal sample and the background of the metal sample by subtracting a preset value for each type of the metal sample from the background concentration peak value. Setting a threshold value for identification, and binarizing the stored density information based on the threshold value, and extracting at least a shape of the non-metallic inclusion, Inspection Method. 2. A microscope for enlarging a metal sample including non-metallic inclusions, an imaging means for converting a metal sample surface magnified by the microscope into a video signal, and a density of each pixel constituting the video signal. A storage unit for storing density information of each pixel multivalued by the converter; and a background of non-metallic inclusions on the metal sample surface using the density information of each pixel. Background density peak value detecting means for detecting a density peak value, and subtracting a value preset for each type of the metal sample from the background density peak value detected by the background density peak value detecting means, Threshold setting means for setting a threshold for identifying a non-metallic inclusion included in a metal sample and a background of the metal sample; binarizing the stored concentration information based on the threshold, Nonmetallic inclusions inspection apparatus characterized by comprising a non-metallic inclusion analysis means for extracting a shape of non-metallic inclusions.