JP6860923B2 - Surface roughness judgment device and judgment method using a white light source - Google Patents

Description

The present invention relates to a surface roughness determination device and a determination method using a white light source, particularly a determination device for evaluating the degree of coincidence of surface roughness and a method thereof.

Various inventions have been proposed for polishing fine irregularities on a surface, for example, a metal surface (http://www.chemicoat.co.jp/column/detail_6.html).

The "chemical polishing" in the above invention is a polishing method in which a metal is immersed in a polishing solution and the surface of the metal is corroded by the chemical reaction by the force of an acid or an alkali. Since the solution penetrates into even the smallest parts, it is possible to process even the smallest details that cannot be reached by mechanical polishing. Chemical polishing is suitable mainly for polishing fine parts with complicated shapes and inner surface parts.

"Electropolishing" is a polishing method in which a metal is immersed in a solution for electropolishing and an electric current is passed through the solution with the metal as a plus. The metal gradually melts and the surface becomes glossy and smooth. It is used when a clean polishing method for precision parts is required in addition to chemical polishing because it does not form an altered layer on the surface and does not leave stains or burns.

As the name implies, "mechanical polishing" refers to polishing performed using a machine, and there are various methods. The current mainstream is a rotary type in which a polishing pad is attached on a disk-shaped surface plate, and a liquid abrasive containing chemical components and fine particles is hung on the polishing pad and polished while rotating. In addition, there are various polishing methods such as polishing with a honing machine that precisely polishes the inner diameter of metal and buffing using a buff.

What is satin finish (Nashijiage) (http://www.chemicoat.co.jp/knowledge/detail_169.html) is a surface that gives a rough surface roughness by adding fine irregularities to the metal surface. Say the processing method. It is called this because it resembles the surface of a fruit pear. The satin finish includes glossy, semi-glossy, and matte satin finishes. The purpose of the satin finish is to improve the surface roughness and appearance, as well as to prevent slipping when touched with oily hands, and to pre-treat when painting or anodizing. It is used in. Further, in order to reduce the frictional resistance on the surface of a precision machine or the like, a process called "scraping" may be performed to make fine irregularities.

There are mainly mechanical and chemical methods for satin finish processing of metal surfaces. Mechanical methods include the above-mentioned shaving process, wire brush method for polishing the surface with a wire brush, and compression of fine particles. There are sandblasting method in which the surface is sprayed with air, and liquid honing method in which a processing liquid containing fine particles is sprayed on the metal processing surface. Examples of the method of chemically performing satin finish processing include a method of using chemical corrosion such as etching and electrolytic corrosion, and a method of performing surface treatment using electroplating such as a dispersion plating method.

Conventionally, there is a roughness meter as a surface gloss / roughness measurement method, and there are a mechanical type and an optical type such as a laser. These are indicated by numerical values such as how many mm or nm the unevenness is, but engineers with experience in the field judge by looking at the glossiness and shine of the surface rather than these values. There is. In addition, work is progressing based on on-site judgment based on such experience. Conventional techniques for measuring surface roughness include the following inventions.

The inventions of Patent Documents 1 to 3 have been proposed in which the surface roughness is measured by a stylus or the like by a copying method.

The inventions of Patent Documents 4 to 6 have been proposed in which the surface roughness is measured by a non-contact image pickup device.

Inventions of Patent Documents 7 to 10 have been proposed in which the surface roughness is measured by a non-contact laser beam.

Japanese Unexamined Patent Publication No. 2014-32177 Japanese Unexamined Patent Publication No. 2014-81324 JP-A-2015-169641 Japanese Unexamined Patent Publication No. 2011-31370 Japanese Patent No. 4204879 Japanese Unexamined Patent Publication No. 2011-69688 Japanese Unexamined Patent Publication No. 2001-4351 Japanese Patent No. 2556556 Japanese Unexamined Patent Publication No. 6-307840 JP-A-2015-143666

However, Patent Documents 1 to 10 cannot determine the surface roughness indicating the finished condition such as gloss, gloss, and roughness. In Patent Documents 1 to 3, since the surface is scanned by a contactor, surface measurement requires time and the apparatus becomes large-scale. Patent Documents 4 to 6 depend on visible light and the like, but their accuracy is not sufficient. In Patent Documents 7 to 10, it takes time to scan the surface with a laser beam and measure the surface roughness.

In addition, when looking at the color of a glossy object, the object color and the light source color are usually recognized in a well-balanced manner. The brightness becomes high where the total reflection occurs, and you can see the color that strongly reflects the light source color. On the other hand, the shade portion has low brightness and is greatly affected by the reflection of the target object. The middle between the highlight and the shade appears to the human eye as a mixture of colors.

Even in the invention using the image of the conventional method, those having a structure such as grain are not merely object colors, and despite the fact that total internal reflection occurs structurally, ΔE, ΔL, Δa, Δb, etc. have a color spread. Since the information was lost and only the average color of a very limited area could be obtained, the texture information woven with various colors and brightness was lost due to the complicated structure. The values varied depending on the measurement location.

Therefore, the surface is roughened or mirror-finished to finish the surface, but the degree of roughness needs to be evaluated by an expert's eyes.

If the surface is fine, the roughness is judged on the order of microns, and if the surface is mirrored, the roughness is judged on the order of nano. However, since the human eye makes judgments on both sides, there is a problem that it takes time and cost and it is difficult to make an accurate judgment. In particular, when the height of the unevenness of the surface roughness is fine and minute, for example, when the wavelength of light is from the nano level (for example, 700 nm) to the micrometer level, it is considered that there is a color shift. There is also a request for fine roughness judgment, but it has not been realized.

For example, when a resin is coated on the surface of a metal, the surface is measured so that the bond between the metal and the resin is optimized, and then the coating is performed. It is impossible to check the roughness of such a small order on the production line with a conventional roughness meter, which is a bottleneck in technological development. This is because it takes a long time to trace the surface with a conventional roughness meter. For example, repeating a line scan on the surface with an AFM or a laser to scan and measure the entire area takes too much measurement time and the line stops. In addition, the surface of the product often has a gentle curve, and many industrial products have a large measurement area, so even if you try to check the surface roughness, it is impossible to measure it in the product. ..

Therefore, in the present invention, the light from the light source is applied to the unevenness of the surface by the XYZ type image pickup device, the reflected light distribution is measured, and the light from the light source is scattered and diffracted depending on the shape of the surface. It is observed as a degree, and it is an object to make it possible to quantify the finish judgment of the surface in a state close to the judgment condition of the human eye by this roughness.

Further, the present invention enables the height of the unevenness of the surface roughness to determine the fine roughness from the nano level to the micro level, shortening the measurement time, determining the roughness of the curved surface, and roughening the product having a large area. The task is to make an accurate judgment.

The present invention is a determination method using an XYZ-based imaging device, and this method can make a determination that is extremely close to the determination method of human appearance. The rationale for this is that in an imaging device, accurate values are obtained in two or three dimensions, so surface roughness such as irregularities on the inspection surface can be quantified by scattering and diffraction phenomena of illumination light.

When the surface exposed to the light from the light source is imaged by the XYZ system imaging device of the present invention and the reflected light distribution is measured, the light from the light source is scattered and diffracted as the color / surface roughness due to the shape of the surface. It was observed that it was possible to quantify the surface color and surface roughness.

The first invention has three spectral sensitivities acquired by an image pickup apparatus having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted to the CIE XYZ color difference function. A calculation processing unit that converts the three-band visual sensitivity images S1i, S2i, and S3i into tristimulus values X, Y, and Z in the CIE XYZ color system and calculates the calculation is provided. The color difference calculation unit that calculates the color difference ΔE of the reference surface and the inspection area of the coordinates corresponding to the color space of the XYZ color system are divided by a grid, and the number of pixels of the inspection plane and the reference plane belonging to each grid are integrated. By doing so, the color space histogram distribution creation unit that creates the color space histogram distribution of the XYZ color system and the surface roughness index M that indicates the difference between the two color space histogram distributions of the inspection surface and the reference surface are examined. There is offset correction performed by identifying the centers of the two color space histogram distributions, the surface and the reference surface, and offsetting the center of one of the color space histogram distributions so that it is close to the center of the other color space histogram distribution. or wherein the surface roughness index calculator for calculating without offset correction, and measured by roughness meter, the surface roughness measurement data storage unit for storing a surface roughness measurement Ra of the test samples according to the calibration, the front surface The roughness evaluation index Est is the surface roughness index M of the inspection surface of the inspection sample related to the calibration and the reference surface of the reference sample, the first coefficient a related to the surface roughness index M, and the inspection of the inspection sample related to the calibration. It is set to be calculated based on the color difference ΔE between the surface and the reference surface of the reference sample and the second coefficient b related to the color difference ΔE, and the error between the surface roughness measurement value Ra and the surface roughness evaluation index Est. The first calibration line function, wherein the first coefficient a and the second coefficient b are determined so that the Error is minimized, and the correlation between the surface roughness measurement value Ra and the surface roughness evaluation index Est is shown. The second calibration line function showing the correlation between the surface roughness measurement value Ra and the color difference ΔE, and the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated without the offset correction. At least one of the three calibration line functions or the fourth calibration line function showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated with the offset correction is set . The surface roughness index M and the color difference ΔE used for the second to fourth calibration lines are the surface roughness index M of the inspection surface of the inspection sample related to calibration and the reference surface of the reference sample, respectively, and the inspection sample related to calibration. Inspection The function setting unit that is the color difference ΔE between the inspection surface and the reference surface of the reference sample, the surface roughness index M of the inspection surface of the inspection sample related to the judgment and the reference surface of the reference sample, and the inspection surface and the reference sample of the inspection sample related to the judgment. Based on the color difference ΔE of the reference surface of the above, the first coefficient a determined by the function setting unit, and the second coefficient b, the surface roughness evaluation index Est'of the inspection surface of the inspection sample according to the determination is calculated. It is a surface roughness determination device using a white light source, which is characterized by having a calculation unit.

In the first invention, the offset correction identifies the centers of two color space histogram distributions of the inspection surface and the reference surface, and the center of one of the color space histogram distributions is close to the center of the other color space histogram distribution. It is characterized in that it is performed by offsetting so as to do so. Further, in the first invention, the surface roughness evaluation index Est has a first coefficient related to the surface roughness index and a first coefficient related to the color difference so that an error between the surface roughness evaluation index Est and the measured surface roughness value Ra is minimized. 2 It is characterized in that it is determined by specifying a coefficient. For example, an analysis method in which the surface roughness evaluation index Est is calculated by using the surface roughness index M and the color difference ΔE by multiple regression analysis is preferable.

The second invention having a technical relationship is obtained by an image pickup apparatus having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted to be equivalent to the CIE XYZ color difference function. Three-band visual sensitivity images with two spectral sensitivities S1i, S2i, S3i are converted into tristimulus values X, Y, Z in the CIE XYZ color system and calculated, and the inspection surface of the inspection sample and the reference of the reference sample. The color difference calculation step for calculating the color difference ΔE of the surface and the inspection area of the coordinates corresponding to the color space of the XYZ color system are divided by a grid, and the number of pixels of the inspection plane and the reference plane belonging to each grid are integrated. The color space histogram distribution creation step for creating the color space histogram distribution of the XYZ color system and the surface roughness index M indicating the difference between the two color space histogram distributions of the inspection surface and the reference surface are set with the inspection surface. With offset correction, or by identifying the centers of two color space histogram distributions on the reference plane and offsetting the center of one color space histogram distribution closer to the center of the other color space histogram distribution. and the surface roughness index calculation step of calculating without the offset correction was measured by roughness meter, the surface roughness measurement data storage step of storing the surface roughness measurement Ra of the test samples according to the calibration, the table surface roughness The evaluation index Est is the surface roughness index M of the inspection surface of the inspection sample related to the calibration and the reference surface of the reference sample, the first coefficient a related to the surface roughness index M, and the inspection surface of the inspection sample related to the calibration. It is set to be calculated based on the color difference ΔE of the reference surface of the reference sample and the second coefficient b related to the color difference ΔE, and the error Error between the surface roughness measurement value Ra and the surface roughness evaluation index Est is The first calibration line function, which defines the first coefficient a and the second coefficient b so as to be the minimum and shows the correlation between the surface roughness measurement value Ra and the surface roughness evaluation index Est, the surface roughness. The second calibration line function showing the correlation between the measured value Ra and the color difference ΔE, and the third calibration showing the correlation between the surface roughness measured value Ra and the surface roughness index M calculated without the offset correction. At least one of the line function or the fourth calibration line function showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated with the offset correction is set, and the first The surface roughness index M and the color difference ΔE used for the 2nd to 4th calibration lines are the surface roughness index M of the inspection surface of the inspection sample and the reference surface of the reference sample related to the calibration, respectively. A function setting step that is a color difference ΔE between the inspection surface of the inspection sample related to calibration and the reference surface of the reference sample, the surface roughness index M of the inspection surface of the inspection sample related to judgment and the reference surface of the reference sample, and the inspection sample related to judgment. Surface roughness evaluation of the inspection surface of the inspection sample according to the determination based on the color difference ΔE between the inspection surface and the reference surface of the reference sample, the first coefficient a and the second coefficient b determined by the function setting step. a surface roughness determination method according to the white light source, characterized by comprising a calculating step of calculating the index Est ', a.

In the second invention, the offset correction identifies the center of two color space histogram distributions of the inspection surface and the reference surface, and the center of one of the color space histogram distributions is close to the center of the other color space histogram distribution. It is characterized in that it is performed by offsetting so as to do so.
In the second invention, the surface roughness evaluation index Est has a first coefficient related to the surface roughness index and a second coefficient related to the color difference so that the error between the surface roughness evaluation index Est and the measured surface roughness value Ra is minimized. It is characterized in that it is determined by specifying.

The "imaging device" divides the reference plane and the inspection plane into three channels for imaging according to the three spectral sensitivities (S ₁ (λ), S ₂ (λ), S _{3 (λ)).} As the means, any of an optical filter, a dichroic mirror, a dichroic prism, or the like set to obtain these spectral sensitivities can be used.

The spectral sensitivity (S ₁ (λ), S ₂ (λ), S ₃ (λ)) of the “imaging device” is a chevron with a single peak that does not have a negative value from the CIE XYZ spectral characteristics, and each of them. peak value of the spectral sensitivity curve is equal and the overlap of the curve of the spectral sensitivity be one obtained by equivalently converting the condition of minimizing, curves spectral characteristics S _1, the peak wavelength of 582 nm, half width It is 523 to 629 nm and has a 1/10 width of 491 to 663 nm. Curve of the spectral characteristics _{S 2} is a peak wavelength of 543 nm, the half value width is 506~589nm, 1/10 width is 464～632Nm. Curve of the spectral characteristics _{S 3} is a peak wavelength of 446 nm, the half value width is 423~478nm, 1/10 width is 409～508Nm.

The "surface" includes a resin surface, a metal surface, a ceramic surface, and the like. Examples of the surface of the resin include a textured surface and the like, and examples of the metal surface include the surface of a cutting tool, the surface of a mold, and the surface of plating. The surface includes not only a flat surface but also a curved surface, and also includes a surface having a complicated shape.

"Evaluation" refers to evaluating the surface roughness. The "index" can be in various forms, such as an index, a graph, a picture, or a combination thereof, not limited to a plane or a three-dimensional object.

"Roughness meter" includes atomic force microscope (AFM), laser roughness meter (for example, for measurement values on the order of nanometers), contact mechanical roughness meter (for example, for measurement values on the order of microns), etc. There are, and these measuring machines measure the measured values. There is a non-contact type or contact type roughness meter of the atomic force microscope AFM (Atomic Force Microscope), which detects the force acting between the atom of the sample and the probe to obtain an image, and is highly accurate. For example, a scanning probe microscope (SPM) capable of measuring micron or nano-order level roughness is exemplified. The measurement method is appropriately selected according to the roughness level of the object to be measured.

The "first calibration curve function, the second calibration curve function, and the third calibration curve function" may be either a straight line function or a curved line function.

It is preferable to set one calibration curve for one reference plane. This is because the standard of the calibration curve such as the mechanical type and the laser type differs depending on the degree of unevenness of the reference surface.

In the claims, the XYZ color system means a broad one including other CIE color systems, and outside the claims, it means a narrow one. The XYZ color system in a narrow sense is defined so that a negative value does not appear in an RGB color system by a simple first-order conversion, and other CIE color systems such as Yxy, XYZ, Lab, Luv, etc. It is a concept that forms the basis of the CIE color system of the above and includes a two-dimensional chromaticity diagram or a three-dimensional color space. Therefore, the XYZ color system in the broad sense includes the XYZ color system in the narrow sense and other CIE color systems developed from the XYZ color system.

The XYZ color system in a narrow sense is defined by the CIE in 1931 at the same time as the RGB color system so that a negative value does not appear in the RGB color system by a simple first-order transformation.

The xyY color system (also referred to as the Yxy color system) is defined to express an absolute hue from the XYZ color system because it is difficult to understand the relationship between numerical values and colors in the XYZ color system. ..

The Luv color system is one of the uniform color spaces defined by CIE in 1976, and CIELuv improves the uniformity of the wavelength interval of the xy chromaticity diagram of the XYZ color system based on the wavelength of light. Is. In Japan, it is stipulated in JIS Z 8518.

The Lab color system is CIELab, which is derived from the XYZ color system to measure the color difference due to the difference between perception and device. In Japan, it is stipulated in JIS Z 8729. A calibration curve based on the Lab color system may be effective for nano-order roughness.

The XYZ color system in a broad sense includes a color space defined by two-dimensional coordinates and three-dimensional coordinates. Typical examples of the color space include configuration examples such as an xy color space, an XYZ color space, and a Lab color space. In the case of a two-dimensional color space, for example, in the case of a Yxy color space and a Luv color space, an xy chromaticity diagram (xy chromaticity value (plane) normalized in the Yxy color space) and a uv chromaticity diagram which are two-dimensional planes. , U'v'chromaticity diagram. The xy chromaticity histogram distribution or the Luv chromaticity histogram distribution, which is expressed as the density of pixels in the two-dimensional chromaticity diagram on a plane, corresponds to this. In the case of a three-dimensional color space, for example, an XYZ color space, and in the case of a Lab color space, a Lab color space can be mentioned. The XYZ color space histogram distribution expressed as the density of pixels in the color space in three dimensions, the Lab color space histogram distribution, and the like correspond.

In contrast to the separation of color and surface roughness in the two-dimensional xy chromaticity diagram, uv chromaticity diagram, and uv'chromaticity diagram, other XYZ color spaces and Lab color spaces are on the three-dimensional color space. It is the separation of color and surface roughness. Therefore, as a term, the xy chromaticity histogram is defined separately as in the XYZ color space histogram, the Lab color space histogram, and the like.

The "XYZ color space histogram" and the "Lab color space histogram" are different from each other, and the surface roughness index calculation in the Lab color space converts the XYZ color space data into the Lab color space data, and this It is calculated from the converted data.

Regarding the mode of offset correction so as to be close to each other, for example, the centers of the histogram distribution are matched, the centers are brought close to each other within a predetermined range, the centers are brought close by moving in parallel with the coordinate axes, and the centers are brought close to each other in a linear direction from the center to another center. It can be carried out in various modes as long as an appropriate index can be obtained, such as close proximity.

The index can be in various modes, not limited to a plane or a three-dimensional object, such as an exponent, a graph, a picture, or a combination thereof.

Since the surface roughness can be quantified by the XYZ-based imaging device, the present invention has an advantage that the judgment very close to the judgment method of human appearance can be performed accurately and efficiently. The evaluation time of surface roughness can be shortened. Roughness on curved surfaces can be evaluated.

The present invention has the advantage of being able to accurately and efficiently evaluate nano-order or micron-order roughness.

It is a block diagram of the surface roughness determination apparatus 1 by the white light source of Embodiment 1 of this invention. This is a function indicating the spectral sensitivity of the two-dimensional colorimeter 2 of the XYZ color system according to the first embodiment of the present invention. This is a specific example of a method of acquiring image information according to three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) in the first embodiment of the present invention. (A) is an explanatory view when a dichroic mirror is used. (B) is an explanatory diagram when a filter turret is used. (C) is an explanatory view when the optical filters 22a, 22b, and 22c are microscopically attached to the image pickup device 23. It is a flowchart in 2D color meter 2 of Embodiment 1 of this invention. It is a flowchart in the arithmetic processing part 3 of Embodiment 1 of this invention. It is a sub-chart in the arithmetic processing unit 3 of the first embodiment of the present invention. (A) is an explanatory diagram showing an inspection region T on the surface 5 of the first embodiment of the invention, (b) is an xy chromaticity diagram showing an inspection region K on a chromaticity diagram corresponding to the inspection region T, and (c) is a lattice. An explanatory diagram of the inspection area K partitioned by G, (d) is a schematic diagram showing the state of overlapping chromaticities on the xy2D chromaticity diagram, (e) is an explanatory diagram showing a minimum distribution, and (f) is an explanatory diagram. It is explanatory drawing which shows an example of the xy chromaticity histogram distribution. (A) is an explanatory diagram showing the roughness of the surface, (b) is an xy chromaticity histogram distribution diagram, and (c) is a three-dimensional image diagram of an xy chromaticity histogram distribution. It is a three-dimensional and plane explanatory view of the surface roughness index. It is a flowchart (calculation of multiple regression analysis) in the arithmetic processing unit 103 of the surface roughness determination apparatus 101 of Embodiment 2 of this invention. It is a block diagram which shows the structure of the surface roughness determination apparatus 101 of Embodiment 2 of this invention. It is a flowchart (XYZ color space distribution) in the arithmetic processing unit 103 of the surface roughness determination device 101 of the second embodiment of the present invention. It is a flowchart (Lab color space distribution) in the arithmetic processing unit 103 of the surface roughness determination device 101 of the second embodiment of the present invention. It is a block diagram which shows the structure of the surface roughness determination apparatus 201 of Embodiment 3 of this invention. It is explanatory drawing which shows the offset correction in the xy chromaticity diagram in the arithmetic processing unit of Embodiment 4 of this invention. It is a flowchart (XYZ color space distribution) in the arithmetic processing unit of Embodiment 4 of this invention. It is a flowchart (Lab color space distribution) in the arithmetic processing unit of Embodiment 4 of this invention. It is explanatory drawing which shows the offset correction in the XYZ color space of Embodiment 4 of this invention. It is explanatory drawing which shows the offset correction in the Lab color space of Embodiment 5 of this invention. It is a graph of the surface roughness evaluation index Est with respect to the surface roughness measurement value Ra of the metal sample 1 of Example 1. FIG. It is a graph of the surface roughness measurement value Ra with respect to the color difference ΔE of the metal sample 1 of Example 1. FIG. It is a graph of the surface roughness measurement value Ra with respect to the surface roughness index M of the metal sample 1 of Example 1. FIG. It is a graph of the surface roughness evaluation index Est with respect to the surface roughness measurement value Ra of the metal sample 2 of Example 1. FIG. It is a graph of the surface roughness measurement value Ra with respect to the color difference ΔE of the metal sample 2 of Example 1. FIG. It is a graph of the surface roughness measurement value Ra with respect to the surface roughness index M of the metal sample 2 of Example 1. FIG. It is explanatory drawing which shows the state of the reflection of the surface of the resin sample 3 of Example 2. FIG. It is explanatory photograph figure which shows the resin sample 3 of Example 2. It is explanatory photograph figure which shows the measurement range and the measurement place (in the image frame) of the resin sample 3 of Example 2. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 41 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 42 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 43 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 44 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 45 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 46 of Example 3. It is a three-dimensional figure which shows the measurement result of the AFM of the metal sample No. 47 of Example 3. It is a graph which shows the relationship between the surface roughness index M of Example 3 and the surface roughness measurement value Ra. It is a graph of the surface roughness evaluation index Est with respect to the surface roughness measurement value Ra of the metal sample No. 41-47 of Example 3. FIG. It is a graph which shows the data for making the 4th calibration curve of Example 4. It is a graph of the Ra value measured by the roughness meter with respect to the Ra value obtained from the surface roughness index M of Example 4.

The surface roughness determination device 1 using a white light source according to a preferred embodiment 1 of the present invention will be described with reference to FIGS. 1 to 12.

The surface roughness determination device 1 has a surface 5 of a two-dimensional colorimeter 2 having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalent to the CIE XYZ color matching function. It is provided with an arithmetic processing unit 3 that acquires and calculates data obtained by converting three-band visual sensitivity images S1i, S2i, and S3i having three spectral sensitivities obtained from the above into tristimulus values X, Y, and Z in the CIE XYZ color system. The calculation processing unit 3 divides the color difference calculation unit that calculates the color difference ΔE and the inspection area of the coordinates corresponding to the color space of the XYZ color system by the grid G, and the inspection plane and the reference plane belonging to each grid G. A color space histogram distribution creation unit that creates a color space histogram distribution of the XYZ color system by integrating the number of pixels, and a surface roughness index M that indicates the difference between the two color space histogram distributions of the inspection surface and the reference surface. Correlation between the surface roughness index calculation unit to be calculated, the surface roughness measurement data storage unit that stores the surface roughness measurement value Ra measured by the roughness meter, and the surface roughness measurement value Ra and the surface roughness evaluation index Est. The first calibration line function L1 showing the above, the second calibration line function L2 showing the correlation between the surface roughness measurement value Ra and the color difference ΔE, or the correlation between the surface roughness measurement value Ra and the surface roughness index M is shown. It is characterized by including a function setting unit for setting at least one of the third calibration line functions L3. The surface roughness evaluation index Est specifies the first coefficient a related to the surface roughness index M and the second coefficient b related to the color difference ΔE so that the error Error with the measured surface roughness Ra is minimized. ..

The spectral sensitivity of the two-dimensional colorimeter 2 satisfies the router condition, and the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is obtained from the XYZ color function as shown in FIG. , It is a chevron shape that does not have a negative value and has a single peak, and is equivalently converted from the condition that the peak values of the respective spectral sensitivity curves are equal and the overlap of the spectral sensitivity curves is minimized. Specifically, the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) has the following characteristics.
Peak wavelength Half width 1/10 width S1 582nm 523-629nm 491-663nm
S2 543nm 506-589nm 464-632nm
S3 446nm 423-478nm 409-508nm

The peak wavelength of the spectral characteristic S1 can be treated as 580 ± 4 nm, the peak wavelength of the spectral characteristic S2 can be treated as 543 ± 3 nm, and the peak wavelength of the spectral characteristic S3 can be treated as 446 ± 7 nm.

The three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are obtained by using the following equation 1. For details on the spectral characteristics themselves, refer to Japanese Patent Application Laid-Open No. 2005-257827.

The specifications of the two-dimensional color meter 2 are, for example, the two-dimensional color meter RC-500 of Paparabo Co., Ltd., which has an effective frequency value of about 5 million pixels, an effective area of 9.93 mm × 8.7 mm, an image size of 3.45 μm × 3.45 μm, and a video. Output 12 Bit, camera interface GigE, number of frames (when focusing) 3 to 7 frames / sec, shutter speed 1 / 15,600 sec to 1/15 sec, integration time up to 3 seconds, S / N ratio 60 dB or more, lens mount F mount, The operating temperature is 0 ° C to 40 ° C, and the operating humidity is 20% to 80%.

As shown in FIG. 1, the two-dimensional colorimeter 2 has a photographing lens 21, three optical filters 22a, 22b, and 22c arranged behind the photographing lens 21, and behind the optical filters 22a, 22b, and 22c. It includes an arranged image pickup element 23 (CCD, CMOS, etc.). The three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) of the two-dimensional colorimeter 2 are determined by the product of the spectral transmittances of the optical filters 22a, 22b and 22c and the spectral sensitivities of the image sensor 23. Given. The arrangement relationship between the optical filters 22a, 22b, 22c and the image sensor 23 in FIG. 1 is merely schematically shown. Specific examples of the method of acquiring the three-band visual sensitivity images S1i, S2i, and S3i according to the three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) are given below. Any of these methods can be adopted, and other methods can also be adopted. Reference numeral 24 is a calculation unit, and 25 is a display unit.

The method shown in FIG. 3A is a method using a dichroic mirror. This reflects light of a specific wavelength by the dichroic mirror 22c', reflects and disperses the remaining light transmitted by another dichroic mirror 22a' by reflecting light of another specific wavelength, and the imaging elements 23a, 23b. , 23c are read out in parallel. Here, the dichroic mirror 22a'corresponds to the optical filters 22a and 22b, and the dichroic mirror 22c' corresponds to the optical filter 22c. As for the light incident from the photographing lens 21, the light according to the spectral sensitivity S3 is reflected by the dichroic mirror 22c', and the remaining light is transmitted. The light reflected by the dichroic mirror 22c'is reflected by the reflecting mirror 26, and the visual sensitivity image S3i is obtained by the image sensor 23c. On the other hand, as for the light transmitted through the dichroic mirror 22c', the light according to the spectral sensitivity S1 is reflected by the dichroic mirror 22a', and the light according to the remaining spectral sensitivity S2 is transmitted. The light transmitted through the dichroic mirror 22a'is imaged by the image pickup device 23b to obtain a visual sensitivity image S2i. The light reflected by the dichroic mirror 22a'is reflected by the reflecting mirror 29, and the visual sensitivity image S1i is obtained by the image sensor 23a. Instead of the dichroic mirror, a dichroic prism having the same characteristics may be used for splitting into three, and the image pickup elements 23a, 23b, and 23c may be adhered to the positions where the respective lights are transmitted.

The method shown in FIG. 3B is a method using the filter turret 27. Optical filters 22a, 22b, and 22c are provided on a filter turret 27 having the same direction as the incident light from the photographing lens 21 on the rotation axis, and these are mechanically rotated. Images S1i, S2i, S3i are obtained.

The method shown in FIG. 3C is a method in which the optical filters 22a, 22b, and 22c are microscopically attached to the image pickup device 23. The optical filters 22a, 22b, and 22c on the image pickup device 23 are provided in a Bayer array type. In this arrangement, the optical filter 22b is provided in half of the region on the image sensor 23 divided in a grid pattern, and the optical filter 22a and the optical filter 22c are evenly arranged in the other half region. That is, the arrangement amount is optical filter 22a: optical filter 22b: optical filter 22c = 1: 2: 1. It is not particularly hindered in the first embodiment that the arrangement of the optical filters 22a, 22b, 22c is other than the Bayer arrangement. Since each of the optical filters 22a, 22b, and 22c is very fine, they are attached to the image pickup device 23 by printing. However, the present invention does not mean this arrangement, but a filter having spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is attached to the image sensor.

The two-dimensional colorimeter 2 transmits the image information acquired by the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) to the arithmetic processing unit 3, and the arithmetic processing unit 3 causes the tristimulus in the XYZ color system. A display device 7 is provided which converts the image data into values X, Y, and Z, performs arithmetic processing by conversion processing on the acquired image data according to the tristimulus values X, Y, and Z, and displays the visualized image.

The arithmetic processing unit 3 calculates the roughness, the color difference, and the like at an arbitrary position of the image acquired by the two-dimensional colorimeter 2 and performs visualization processing. Illumination is performed from directly above or diagonally above the surface 5, and xy, XYZ, or Lab chromaticity distribution data are compared and indexed.

The surface 5 is usually imaged at one place by the two-dimensional colorimeter 2, and the two-dimensional colorimeter 2 is moved to image at another angle as needed. Here, for example, it is possible to take a picture at three places (a number of places may be appropriate) of the front and 45 degrees to the left and right.

A xenon lamp (pseudo-sunlight) is used as the illumination source of the illumination unit 6. The illumination unit 6 includes a Fresnel lens assembly in addition to the xenon lamp. The xenon lamp shall illuminate uniformly from diagonally above the surface 5. In addition to the xenon lamp, an LED artificial sun lamp may be used.

The display device 7 is connected to the arithmetic processing unit 3, receives the image signal processed by the arithmetic processing unit 3, and displays the image on the screen. The arithmetic processing unit 3 or the display device 7 is appropriately provided with input means (not shown) and the like. The input means is a keyboard, a mouse, a touch panel provided on the display, or the like.

The operation of the surface roughness determination device 1 on the surface 5 will be described with reference to specific examples. As shown in FIG. 1, the surface roughness determination device 1 of the surface 5 operates by connecting the two-dimensional colorimeter 2, the arithmetic processing unit 3, and the display device 7. The connection method can be selected regardless of whether it is wired or wireless. The flowchart of the surface roughness determination device 1 is shown in FIG. 4, and the flowchart of the arithmetic processing unit 3 and the display device 7 is shown in FIG. 5, respectively.

When the power of the two-dimensional colorimeter 2 is turned on, initialization is performed as shown in FIG. 4 (initialization S1). Next, the surface 5 is imaged (imaging process S2), and then the captured three-band visual sensitivity images S1i, S2i, and S3i are input by the image sensor 23 (input process S3), and the arithmetic processing unit 3 performs tristimulation. It is converted into the values X, Y, and Z (conversion process S4). The three-band visual sensitivity images S1i, S2i, and S3i are transmitted to the display device 7 (data transmission S5). When the image is a moving image, a series of processes from the imaging process S2 to the data transmission S5 are continuously performed (S6). The image is displayed on the display device 7.

The conversion formulas from the tristimulus values X, Y, Z to the Y'xy color system are given in Equations 2 and 3. Here, a luminance meter (not shown) was used together with the two-dimensional colorimeter 2, and Y was calibrated by the value (nt) of the luminance meter to be Y'. Since the color space conversion formula is used, other detailed formulas are omitted.

The XYZ color system is currently the basis of each color system as the CIE standard color system. It was developed based on the principle of additive color mixing of the three primary colors of light (R = red, G = green, B = bluish purple), and the color is represented by the three values of Yxy using a chromaticity diagram. Y is the reflectance and corresponds to the brightness, and xy is the chromaticity.

The imaging process S2 is a step of imaging the surface 5 with a two-dimensional colorimeter 2 having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) (see FIGS. 1 and 4). The spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is given according to the above equation 1. The input process S3 is continuously performed at the same time as the image is taken by the photographing lens 21, the optical filters 22a, 22b, 22c and the image sensor 23.

Since the input image data is a value according to the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)), the conversion process S4 in the arithmetic processing unit 3 connected to the two-dimensional colorimeter 2 causes the input image data. The image data of the captured image is converted into tristimulus values X, Y, and Z. This conversion is performed according to Equation 1. That is, the tristimulus values X, Y, and Z can be obtained by multiplying the inverse matrix of the coefficients in Equation 1. The two-dimensional colorimeter 2 transmits the values according to the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) to the arithmetic processing unit 3.

When the power is turned on to the arithmetic processing unit 3, it is initialized as shown in FIG. 5 (initialization S110). The display device 7 receives the three-band visual sensitivity images S1i, S2i, and S3i transmitted from the two-dimensional colorimeter 2 in a state of being connected to the two-dimensional colorimeter 2 (data reception S120). After that, the three-band visual sensitivity images S1i, S2i, and S3i are converted into tristimulus values X, Y, and Z, and the Lab average value and the xy surface roughness index M of the images of the captured reference surface and the inspection surface are calculated and visually recognized. (S130), if necessary for displaying on the display device 7, color information is converted to RGB or the like (S140). The contents are transmitted to the display device 7 (display process S150). After the data reception S120 from the two-dimensional colorimeter 2, a series of processes from the conversion process S130 to the display process S150 are continuously performed (S160).

The display process S150 is a step of displaying the visualized surface roughness index M on the display device 7, and returns the process.

The sub-flow chart of S140 of FIG. 6 will be described. The first image (image B) of the reference surface is imaged, then the second image (image A) of the inspection surface to be compared is imaged, and the surface roughness index M is sequentially calculated as follows. The similarity of the surface roughness is determined by the surface roughness index M obtained by separating the surface roughness.

The inspection area K (see FIG. 7B) corresponding to the region T (see FIG. 7A) to be inspected is set for the captured images A and B (S141). The size and location can be set freely.

The chromaticity xy is calculated (S142).

An xy chromaticity histogram distribution of the reference plane of the inspection region K cut out from the image B of the captured reference plane is created (S143).

The xy chromaticity histogram distribution is a three-dimensional histogram showing the integrated number of pixels belonging to each unit cell G.

FIG. 7 (f) shows the color distribution of the comparison target at the position of the xy coordinate in a plane, and as shown in FIG. 7 (c), the inspection area K is divided by the grid G, and the xy of the division is divided. A histogram distribution is created by integrating the pixels having values and using the z-axis as the axis. Let the xy coordinates be a grid of a specific width, for example, a plane grid in which xy is cut by 1/1000 (1000 lines). The inspection area K is scanned from one end to the other, and the number of pixels belonging to each area divided in the grid G is integrated in the z direction. Further, if only the specific range of the xy coordinates is calculated in the inspection area K, the calculation time can be shortened. If the grid is made finer, the accuracy will be improved, but the calculation time will be longer, so use appropriate grids.

Similar to S143, an xy chromaticity histogram distribution of the image A of the inspection surface is created (S144). In the xy chromaticity histogram distribution, the xy axis is the xy chromaticity and the z axis is the integrated number of pixels.

For the a-axis, b-axis, and L-axis of Lab, the sum of all pixels in the inspection area K is taken independently, and the sum of each L value, a value, and b value is divided by the number of pixels to divide the Lab. Calculate the average L value, average a value, and average b value of the chromaticity distribution. Further, ΔE is calculated by the mathematical formula 4 (S145).

The Lab value of the Lab space converted by the following formula 5 is calculated. The Lab color space is a kind of complementary color space, has a dimension L meaning lightness and complementary color dimensions A and B, and is based on a non-linearly compressed coordinate of the CIEXYZ color space. The XYZ value before normalization is converted into a Lab value by Equation 5. In contrast to the distribution on the XYZ color space, the distribution in the Lab color space can be obtained in consideration of the brightness direction.

In Equation 5, the values of X, Y, and Z in parentheses of the function f are _{divided by the coordinates X n} , Y _n , and Z _n of the white point, respectively, in order to align the maximum values with 1.

The difference between the average values of the reference surface and the inspection surface is used as a material for judging the difference in color.

The surface roughness index M is calculated (S146). This makes it possible to simply separate the degree of roughness and quantify it. Roughness is calculated in the two-dimensional space of the xy chromaticity distribution, and the difference in roughness can be grasped as the difference in roughness excluding color, so the color and surface roughness are assured. Can be detected separately.

The surface roughness index M is calculated by the following formula. The xy chromaticity histogram distribution is the integrated number of pixels, and the overlapping region D is shown in FIG. 7 (d), and the minimum distribution is shown in FIG. 7 (e).
Surface roughness index M = integrated number of pixels belonging to overlapping area D / total number of pixels in inspection area K x 100 (%)

The xy chromaticity histogram in the two-dimensional space of the reference plane and the inspection plane is calculated, and the minimum value of the same positions in the array is taken as the overlap frequency. It is calculated by dividing by.

7 (d) and 7 (e) are one cross-sectional view of FIG. 7 (c) cut out in the SS cross section, and there is an overlap when viewed on the same line in xy coordinates. Instead of drawing three-dimensionally, it is drawn on a flat surface for convenience. Moreover, since it is a histogram, it has a minute stepped distribution. Cumulative number _{H 1} and the accumulated number of _{H 2} FIG. 7 (d) respectively image A, corresponding to the image B. Comparing the two histogram distributions, there is an overlapping region D.

As shown in FIG. 7 (e), H ₁ (x ₁ , y ₁ ) is the integrated number of the xy chromaticity histogram distribution on the inspection surface, and H ₂ (x ₂ , y ₂ ) is the xy chromaticity histogram distribution on the reference surface. If the integrated number, in overlapping the left area _H 1> _{H 2,} centrally _H 1 _{<H 2,} and the on the right is _H 1> _{H 2.} Among of an H _1, H _2, taking the smaller cumulative number (pixel frequency), the left and right H _2, minimum distribution can be identified is a histogram curve of an H _1, and the stepped in the middle. Using this, the ratio of the overlapping region D to the entire region in two dimensions or three dimensions can be calculated.

The smaller integrated value is specified by this minimum distribution. By adding the smaller integrated number of H ₁ and H ₂ , the integrated number of the overlapping region D can be calculated, and the ratio to the total number of pixels can be specified. The total number of pixels in the inspection area K is fixed, and the total number of pixels is the same on both the inspection surface and the reference surface. The calculation of this ratio may be three-dimensionally integrated for all the grids G. For example, as shown in FIG. 7C, the inspection area K is cut along the SS axis, and y is a predetermined value. _{The distribution of the integrated numbers H 1} and H ₂ of the pixels when x changes from end to end may be integrated two-dimensionally. FIG. 7 (f) is an example of the xy chromaticity histogram distribution. If there is no distribution in the inspection area K and the number of pixels is zero, it is excluded from the calculation.

Finally, display / save processing and transmission processing are performed (S147), and the processing is returned.

For example, the pixels belonging to the inspection area K are defined as 100 vertical pixels x 100 horizontal pixels = 10,000 pixels. Since the image is cut out in the same inspection area K, the total number of pixels of the image A and the image B is 10,000 pixels. The number of pixels in the overlapping region D is integrated from the xy chromaticity histogram, and when the number of pixels in the overlapping region D is 5,000, the surface roughness index M is 50%. The degree of difference in surface roughness increases as the surface roughness index M falls below 100%. If the distribution of xy values is completely the same, it will be 100%. As a result, when it is determined that the numerical value is equal to or higher than a certain value, it can be determined that the surface roughness is a conforming surface.

Since the color information initially obtained for the image is based on three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) which are equivalent functions to the XYZ color matching function, it is acquired by RGB. It is more faithful to the sensitivity of the human eye and more accurate than when it is used. The overlap of the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) is small, the S / N ratio is sufficient, and the curve in the spectral sensitivity curve changes naturally, so the error in color measurement is It is kept to a minimum.

Since the surface roughness of the image can be grasped by the histogram distribution separately from the color, the difference in the roughness of the surface 5 can be accurately determined.

For example, as shown in FIGS. 8A to 8C, an example will be described in which three types of inspections are inspected, from those having a small roughness to those having a large roughness. The one with a small roughness is designated as the reference surface 1, the one with a medium roughness is designated as the inspection surface 2, and the one with a large roughness is designated as the inspection surface 3. First, when the distribution on the xy chromaticity diagram after performing the above processing for 1 to 3 is created, as shown in the xy chromaticity diagram of FIG. 8B, the highlighted portion is the integrated data. .. The integrated numbers H ₁ and H ₂ are shown in light and dark, and the brighter the color, the larger the integrated number. FIG. 8 (c) the cumulative number of H _1, H ₂ as a three-dimensional test surface and the reference plane schematically showing. The xy axis is the chromaticity, and the z axis is the integrated numbers H ₁ and H ₂ . Basically, the stronger the unevenness, the lower and wider the chevron shape, and the weaker the unevenness, the sharper the chevron shape. By comparing the two histogram distributions of the reference surface 1 and the inspection surface 2 or 3, the surface roughness index M indicating the degree of overlap is calculated. The unevenness of micron or nano-order or more can be determined by the surface roughness index M.

In this embodiment, the color is measured not by a point but by a surface, and not only the average value but also the statistical comparison can be performed. Roughness information in which color information is complicatedly woven can be quantified. Each pixel is a colorimetric value. Statistical processing is performed by using a histogram.

The surface roughness index M is a numerical comparison of surface roughness such as grain, and a colorimetric image is taken with a two-dimensional colorimeter. Since there is actually frequency information, a mountain is formed in the three-dimensional chromaticity diagram as shown in the upper part of FIG.

In FIG. 9, (1) is a sharp mountain concentrated on one point, (2) a normal mountain, and (3) is a wide and gentle mountain. Looking at the mountain from the side, it looks like the bottom in Fig. 9. When (2) and (3) are overlapped with reference to (1), the colored part is the overlapping part. The ratio of the volume here is the surface roughness index M. Since the integrated number within the range of the inspection area K is used as it is for the surface roughness index M, the inspection surfaces 2 and 3 are 58% and 27%, respectively, with respect to the reference surface 1, which is clear and easy in numerical values. The surface roughness can be identified.

When the surface roughness is on the nano-order or micron-order, the surface roughness evaluation index M and the color difference ΔE calculated as described above are used, and the surface roughness evaluation index is as shown in FIG. Calculate Est. This is because the light scattering on the surface 5 is different in the case of nano- and micron-order roughness than in the case of large roughness. For fine roughness, interaction with the shining light, that is, interference occurs and various shifts occur, so it is more accurate to step on it positively. Since the wavelength range of visible light is 380 nm to 780 nm, a larger color shift is observed at nano-level surface irregularities close to this wavelength. When the surface irregularities are larger than the micron level compared to the wavelength of light, the diffraction of white light derived from the irregularities is more predominant than the light shift for the same material or different materials, so consider the color shift. You don't have to. Therefore, it was decided to positively utilize the color shift for the surface roughness index M and the color difference ΔE to correlate with the roughness meter by multiple regression analysis.

First, when this process is started, the measured value of the roughness meter for the determination target object is acquired (S150). Here, the surface roughness measurement value Ra is obtained by a contact type roughness meter.

By multiple regression analysis between Ra and Est represented by the following equation 6, the first coefficient a and the second coefficient b are specified so that the sum of the errors Errr by the least squares method is minimized (S151).
(Number 6)
Est = a ・ M + b ・ ΔE
In Equation 6, Est is the surface roughness evaluation index, a is the first coefficient, M is the surface roughness index, b is the second coefficient, and ΔE is the color difference.

Substituting the measured values of M and ΔE into the mathematical formula 6 in which the above a and b are specified, the surface roughness evaluation index Est is calculated (S152).

In some cases, the formula 7 is used instead of the formula 6. This is a simpler method that does not use the parameter of the color difference ΔE. By multiple regression analysis between Ra and Est represented by the following formula 7, the total error coefficient by the least squares method represented by the formula 8 is minimized. The first coefficient a and the second coefficient b are specified so as to be.
(Number 7)
Est = a ・ M + b
In Equation 7, Est is the surface roughness evaluation index, a is the first coefficient, M is the surface roughness index, and b is the second coefficient.
(Number 8)
Error = Σ (Est (i) -Ra (i)) ²
In Equation 8, Error is the error, Est is the surface roughness evaluation index, Ra (i) is the value measured by the roughness meter, and i is the number of measured data.

The measured value of M is substituted into the mathematical formula 7 in which the above a and b are specified, and the surface roughness evaluation index Est is calculated (S152).

From the surface roughness index M, the color difference ΔE, the surface roughness evaluation index Est, and the surface roughness measurement value Ra measured and calculated as described above, the first calibration curve function L1, the second calibration curve function L2, and the third calibration curve function Set at least one of L3. All are set here, but a function with less error among the three, or a combination thereof can be used. If you want to determine the roughness of the surface 5 to be determined, you can take an image of the surface 5 to be determined and set each of the above functions from the calculated surface roughness index M and color difference ΔE. The surface roughness evaluation index Est'and the surface roughness measurement value Ra'(estimated calculation value, not the actual measurement value) can be obtained. As a result, by accurately evaluating the surface roughness, it is possible to appropriately compare and judge the surface roughness with respect to the reference surface, and it is possible to prevent the occurrence of defective products. Details will be described in Examples 1 to 3.

Next, the surface roughness determination device 101 of the surface 105 of the second embodiment will be described with reference to FIGS. 11, 12, and 13. For the corresponding similar elements, the explanation will be referred to as the 100s, and the differences will be mainly explained.

An arithmetic processing unit 103 that connects to a two-dimensional colorimeter 102 that images a reference surface and an inspection surface via a switch 109 and receives a signal to calculate a surface roughness index M, and an arithmetic processing unit. It is provided with a display device 107 that is connected to the 103 and displays an exponent.

As shown in FIG. 11, the arithmetic processing apparatus 103 includes an arithmetic unit 103A that calculates a stimulus value XYZ1 acquired by imaging a reference surface, and a stimulus acquired by imaging the surface 105 of a product to be an inspection surface. A calculation unit 103B for calculating the value XYZ2 and a calculation unit 103C for connecting the calculation unit 103A and the calculation unit 103B and calculating the surface roughness index M of the surface 105 are provided. The OK signal or NG signal from the calculation unit 103C is transmitted to the display device 107 or transmitted to the outside. The switch 109 selectively connects the calculation unit 103A and the calculation unit 103B to the two-dimensional colorimeter 102.

FIG. 12 is a flowchart for calculating the surface roughness index M by comparing the chromaticity histogram distributions from the two images A and B. As shown in FIG. 12, when the program is started, the inspection area K on the inspection surface is cut out from the image A, specified, and set (S201). Next, the inspection area K of the reference plane is cut out, specified, and set from the image B as in the image A (S202). The chromaticity value XYZ is calculated from the images A and B (S203). In the inspection area K, the XYZ color space histogram distributions of the inspection surface and the reference surface are calculated and created (S204). The average value of the XYZ values is calculated (S205). The minimum distribution of the XYZ color space histogram distribution is specified, and the integrated number of the XYZ color space histogram distribution in the overlapping region D is calculated (S206). The surface roughness index M = (integrated number of pixels belonging to the overlapping region D / total number of pixels in the inspection region K) × 100 (%). The integrated number in the overlapping region D is _{calculated by adding the integrated number of T 1} and T ₂ , whichever is smaller. The surface roughness index M is calculated (S207) and returned.

In the case of the calculation of the XYZ distribution corresponding to the inspection area K, the calculation of the surface roughness index M is performed based on the distribution in the three-dimensional space of the X-axis, the Y-axis, and the Z-axis. Let the histograms in XYZ spatial coordinates on the inspection surface and the reference surface be T ₁ (X, Y, Z) and T ₂ (X, Y, Z), respectively. In the XYZ color space, the histogram distribution is shaped like a globe, and the two histogram distributions may be three-dimensionally overlapped or separated. The inspection area K in the three-dimensional space is divided by the lattice G, and the chromaticity histogram distribution and the minimum distribution _{of T 1} (X, Y, Z) and T _{2 (X, Y, Z) in three dimensions are obtained, and the same is true.} Perform exponential calculation. The integrated numbers H ₁ and H ₂ of the grid G may be projected on a plane, and the integrated number of the overlapping region D on the grid G may be calculated by the same integration in the plane. In the case of XYZ chromaticity, since there is no brightness information, the histogram distribution does not change in the XYZ space even if the brightness of the image changes.

When the Lab color space histogram is used for surface roughness determination instead of the XYZ color space histogram, the flowchart of FIG. 13 is used. The description of FIG. 13 is based on the above description of FIG. 12, and the steps are in the 300s. In S305, the average Lab value of the inspection area K of the image A is calculated and the average Lab value of the inspection area K of the image B is calculated. In the case of the Lab chromaticity, since there is brightness information, the histogram distribution changes when the brightness of the images A and B changes in the Lab space.

Next, the surface roughness determination device 201 of the surface 205 of the third embodiment will be described with reference to FIG. For the corresponding similar elements, the explanation is referred to as the 200 series, and the differences are mainly explained.

As shown in FIG. 14, the color / roughness determination target is a part of the surface 205, and the two-dimensional colorimeter 202 images the inspection area K of the surface 205. The arithmetic processing device 203 is connected to the arithmetic unit 203A that calculates the Lab from the reference stimulus value XYZ1, the arithmetic unit 203B that calculates the Lab from the stimulus value XYZ2 to be determined, and the arithmetic unit 203A and the arithmetic unit 203B. A calculation unit 203C for calculating the average value, a surface roughness index M calculation unit 203D for calculating the surface roughness index M from the reference Lab and the target Lab, and a roughness processing device for calculating the calculation values from the calculation units 203C and 203D. Send to 250. The roughness processing device 250 determines whether or not the surface roughness is appropriate by checking the exponential value displayed on the screen, and further performs the roughness processing. The switch 209 selectively connects the calculation unit 203A and the calculation unit 203B to the two-dimensional color meter 202. Since the main processing is almost the same as the flowcharts of the first and second embodiments, the description is incorporated.

In the case of calculation of the chromaticity histogram distribution in the Lab space corresponding to the inspection area K, the XYZ value is converted to the Lab. The exponent is calculated based on the distribution of the L-axis, a-axis, and b-axis in the three-dimensional space. The Lab color space distribution has a three-dimensional elliptical shape. Let the histograms in Lab space coordinates on the inspection surface and the reference surface be U ₁ (L, a, b) and U ₂ (L, a, b), respectively. In the Lab color space, the histogram distribution is shaped like a globe, and the two histogram distributions may be three-dimensionally overlapped or separated. The inspection area K in the three-dimensional space is divided by the lattice G, and the color space histogram distribution and the minimum distribution _{of U 1} (L, a, b) and U _{2 (L, a, b) in three dimensions are obtained, and the same is true.} Perform exponential calculation. The integrated numbers H ₁ and H ₂ of the grid G are projected on a plane, and the integrated number of the overlapping region D on the grid G is calculated by the same integration in the plane. In the case of the Lab color space, since there is brightness information, in the Lab space, when the brightness of the images A and B changes, the L value changes, and the histograms U ₁ and U ₂ are positioned in the Lab space. Therefore, it is possible to make a judgment in consideration of lightness and darkness. This is because if the brightness of the images A and B is different, the position of the distribution will shift. For example, the Lab color space histogram distribution shifts downward when it gets dark, and shifts upward when it gets bright.

Other application examples will be described. The two images A and B obtained from the reference plane and the inspection plane are superposed, the respective chromaticity histogram distributions are displayed on the display device 7, and the respective chromaticity histogram distributions are displayed on one chromaticity diagram. The superimposed chromaticity diagram can be displayed, and the difference in color can be determined by the average Lab value, while the calculation of the surface roughness index M indicating the roughness of the surface 5 can be separated and displayed as a percentage. As a result, it is possible to reliably confirm the unevenness and roughness indicated by the difference between the spatial spread of the chromaticity distribution on the inspection surface and the chromaticity distribution on the reference surface. The inspection result is displayed numerically for each area T. The grid width of the grid G can be adjusted. The index threshold can be set arbitrarily. The measurement results and the captured images A and B can be saved. It is possible to standardize the finish condition of the surface roughness and perform stable surface roughness management by reducing the problems of individual differences that cannot be avoided by visual inspection and the troubles of the judgment criteria with the customer.

The surface roughness determination device and the surface roughness determination method using the white light source of the fourth embodiment will be described with reference to FIGS. 15 to 19. Since it is basically the same as the first to third embodiments, the explanation of the common points will be incorporated.

Regarding the calculation of the surface roughness index M, the first to third embodiments are the cases where the offset correction is not used. This is more accurate roughness data because calibration data is created with both parameters of color change and color distribution change when the material of the data to be calibrated and the material to be measured are the same. Can be sought. On the other hand, the fourth embodiment is a case where an offset value is used. When it is unclear whether the metal material to be measured is the same as the one to be calibrated, this is a method of obtaining a calibration curve based only on the color distribution consistency with the offset value corrected.

In the first to third embodiments, since the metal material or the composite material is made uneven by etching or the like, the original color of the material is the same, so that the calibration substrate of the same material is used. In Example 4, calibration substrates of different materials are used to create the calibration curve. As the calibration substrate, for example, there is a calibration substrate sold as a standard of roughness. When a commercially available calibration substrate is used, if the roughness value is known, the roughness value can be used as the Ra value.

In the fourth embodiment, the procedure for measuring the surface roughness is as follows. a. Measure the Ra value of the calibration substrate with a mechanical roughness meter. b. The surface roughness index M is calculated. Here, when the material to be measured is the same material as that for calibration, the calculation without offset correction is performed, and when the material to be measured is unknown, the offset correction is performed and the calculation is performed.

If the metal material to be measured is the same material as the one to be calibrated, the color change (ΔE, Δb, etc.) is also used as a parameter for the calibration curve (curve), but when the original color of the material is unknown. , The additional function is to offset the colors and create a fourth calibration curve (calibration curve) only with the surface roughness index M (matching degree).

For the data shown in FIG. 38, set a calibration curve of Ra = A / (MB) and use the optional function of the Excel solver (a convenient function to calculate the least squares method with the set function). When A and B were obtained, A = 62.31 and B = 18.526, and a calibration curve of Ra = 62.31 / (M-18.526) was obtained. Ra is a Ra machine measured value and is a surface roughness value specified by JIS. Obtained with a mechanical roughness meter. The definition is in JIS. A and B are calibration coefficients.

Next, the offset correction will be described with reference to FIGS. 15 to 19. As shown in FIGS. 16 and 18, _{the center coordinates of one of the two histogram distributions T 1} (X, Y, Z) and T ₂ (X, Y, Z) should match the other center coordinates. The entire XYZ color space histogram distribution is offset (mapped) by the deviation ΔF of the center coordinates (S407). This is because if one of the distributions is not offset-corrected to the other distribution, the difference in color components will also be calculated. You can do it on the graph or just by calculating. The offset amount can be set as appropriate. For example, instead of the offset correction from one center to the other center, an offset from one center to the other center within a predetermined range has the same effect. The point is that the roughness may be approached with an appropriate offset amount that can be grasped.

Before and after the offset correction (S407), _{steps for specifying the center coordinates C 1} and C ₂ of the XYZ color space histogram distribution are provided (S406, S408). Here, the center coordinate is the center of gravity (center of gravity position). The difference between FIGS. 12 and 16 is that the three steps S406 to S408 are inserted.

When the Lab color space histogram shown in FIG. 19 is used for surface roughness determination instead of the XYZ color space histogram, the flowchart of FIG. 17 is used. The description of FIG. 17 is based on the above description of FIG. Further, when the xy chromaticity histogram is used, the offset is performed as shown in FIG. The flowchart is omitted.

Although the present embodiments 1 to 4 have been described above, they have the following effects. (1) Mean L value, Mean a value, Mean b value, and (2) Two H ₁ (x, y), H ₂ (x, y), T ₁ (X, Y, Z), T ₂ Examples of the color difference ΔE and the surface roughness index M for (X, Y, Z), U ₁ (L, a, b), and U ₂ (L, a, b) are given, but the color and surface roughness are given. By presenting the difference from the above separately, accurate and quick evaluation can be performed, and by adjusting the surface roughness, it is possible to give a proper guideline for the finish condition.

Further, in the present embodiment, the height of the unevenness of the surface roughness can determine the fine roughness from the nano level to the micrometer level, shortening the measurement time, determining the roughness of the curved surface, and determining the area. It is possible to accurately determine the roughness of a large product.

Further, when the material to be measured is unknown, offset correction is performed and the calculation is performed, so that accurate surface roughness can be measured.

In order to evaluate the roughness of the surface 5, the surface roughness determination device 1 of the present invention measured and evaluated as in Examples 1 to 3. The present invention will be specifically described, but the present invention is not limited thereto. The characteristic values in the examples were measured and evaluated as follows.

(1) Judgment device PPLB-200 from Paparabo Co., Ltd. was used. PPLB-200 is equipped with a two-dimensional colorimeter RC-500. The lighting used was Panasonic D50 lighting.

(2) Shooting
The shooting with PPLB-200 was done in a dark room. A two-dimensional colorimeter was used, and the measurement was performed with the L value of the white plate as 100.
(3) Measurement range Regarding the measurement range of the sample, the measurement range is the entire images A and B in Examples 1, 2 and 3.

(4) Roughness Meter Examples 1, 2 and 4 are a roughness meter manufactured by Taylor Hobson, FORM TALYSURF (registered trademark) -120 (AFM contact type) roughness meter, and Example 3 is Keyence's VN-. A non-contact roughness meter of 8010 was used.

(5) Measurement items and results The reference surface of the sample and the inspection surface of the inspection sample were measured. The difference between the surface roughness indexes M, ΔE and the average Lab of the photographed reference / inspection sample images A and B was obtained. The surface roughness index M is a value obtained without offset processing of the center coordinates of the histogram distribution. Difference in average Lab value = average Lab value of inspection sample-average Lab value of reference sample. From the measurement results, from the results of the surface roughness index and the numerical value of ΔE, a sample having the most similar color and surface roughness of the surface 5 to the reference sample was identified. Regarding ΔE, ΔE00 was used in consideration of human visual sensitivity characteristics.

The surface roughness indices M, Lab, ΔL, Δa, Δb, and ΔE of Samples 1 and 2 of Example 1, Sample 3 of Example 2, and Sample 4 of Example 3 were determined. The surface roughness index M is an index calculated without offset processing of the center coordinates of each histogram distribution. Lab was the surface roughness index in the Lab coordinates, M was the surface roughness index in the xy coordinates, ΔL = the difference in the average L value = the average L value of the inspection sample-the average L value of the reference sample. The same applies to ΔE, Δa, and Δb. From the measurement results, the sample 2 or 3 having the most similar color and surface roughness to the sample 1 was identified from the result of the surface roughness index and the numerical value of ΔE. Regarding ΔE, ΔE00 was used in consideration of human visual sensitivity characteristics.

(6) Setting of calibration curve For Example 1, Ra and Est, Ra and ΔE, Ra and M, and for Example 3, the calibration curve between Ra and M and Ra and Est are analyzed by multiple regression analysis. I asked. Multiple regression analysis was performed in Example 1 to evaluate the roughness on the micron order and in Example 3 to evaluate the roughness on the nano order.

For the calibration curve between Ra and Est, equations 6 and 7 were used for Est, and a and b were set so as to minimize the error due to the least squares method by multiple regression analysis.

Example 1 is an evaluation of the roughness of an aluminum alloy treated with chemical etching.

Table 1 and FIGS. 20 to 22 show the measurement / calculation results of sample 1. In FIG. 20, the horizontal axis represents the surface roughness measurement value Ra (unit: μ), the vertical axis represents the surface roughness evaluation index Est, and the straight line indicated by the dotted line is the first calibration curve L1. In FIG. 21, the horizontal axis represents the color difference ΔE, the vertical axis represents the surface roughness measurement value Ra (unit: μ), and the straight line indicated by the dotted line is the second calibration curve L2. In FIG. 22, the horizontal axis represents the surface roughness index M, the vertical axis represents the surface roughness measurement value Ra (unit: μ), and the straight line indicated by the dotted line is the third calibration curve L3.

A = 0 and b = 0.105242 were obtained. The correlation coefficient between the surface roughness evaluation index Est and the surface roughness measurement value Ra is 0.887104, the correlation coefficient between the color difference ΔE and the surface roughness measurement value Ra is 0.887104, and the surface roughness index M and the surface roughness measurement. The correlation coefficient of the value Ra is −0.8377, and the total error TotErro is 5.9516.

Sample 2 of Example 1 is an aluminum alloy that has been chemically etched.

Table 2 and FIGS. 23 to 25 show the measurement of the aluminum alloy sample 2 and the calculation results by the mathematical formula 6 and the like. In FIG. 23, the horizontal axis represents the surface roughness measurement value Ra (unit: μ), the vertical axis represents the surface roughness evaluation index Est, and the straight line indicated by the dotted line is the first calibration curve L1. In FIG. 24, the horizontal axis represents the color difference ΔE, the vertical axis represents the surface roughness measurement value Ra (unit: μ), and the straight line indicated by the dotted line is the second calibration curve L2. In FIG. 25, the horizontal axis represents the surface roughness index M, the vertical axis represents the surface roughness measurement value Ra (unit: μ), and the straight line indicated by the dotted line is the third calibration curve L3.

A = 0.0009893 and b = 0.694288 were obtained. The correlation coefficient between the surface roughness evaluation index Est and the surface roughness measurement value Ra is 0.849651, the correlation coefficient between the color difference ΔE and the surface roughness measurement value Ra is 0.849774, and the surface roughness index M and the surface roughness measurement. The correlation coefficient of the value Ra is −0.88685, and the total error TotErro is 11.60513. In FIG. 23, there is Est = 8.352787 that is off by one point upward, but if it is removed as a measurement error, the correlation coefficient becomes approximately 0.9.

In Tables 1 and 2, when the etching time is increased from No. 11 to No. 19 and No. 21 to No. 29, the surface roughness index is compared with that of the aluminum alloy without chemical etching. As M decreases, ΔE, Ra, and Est increase.

As for the roughness adjustment by chemical etching of aluminum alloy, the surface 5 is initially flat, and when etching is applied, the surface 5 becomes rough, and when further etching is applied, holes are dug or dents are formed on the surface 5. There are various forms of roughness due to the formation of cavities in the surface, but it can be evaluated accurately.

According to the first embodiment, the roughness of the micron order or the nano order can be accurately calculated by the first calibration curve L1 to the third calibration curve L3, and an appropriate unevenness can be grasped. Good products can be reduced. For example, when a resin layer is coated on an aluminum alloy that has undergone chemical etching, the resin layer is likely to be peeled off from the aluminum alloy due to excess or deficiency of etching. By inspecting the surface 5 of the aluminum alloy before coating the resin on the aluminum alloy, the aluminum alloy can be treated appropriately with the most optimum surface roughness so that the aluminum alloy and the resin layer do not peel off. It is possible to avoid the phenomenon that the resin layer is peeled off from the aluminum, and it has high industrial utility value.

The measurement location, degree of agreement, ΔE00, ΔL, Δa, Δb values, etc. of the sample 3 which is the resin component of Example 2 are shown. The multiple regression roughness analysis was performed in the same manner as in Example 1, but the same results were obtained by the same method as in Example 1, so the description is omitted. The measurement range is within the borders of images A and B, respectively.

In the sample 3 shown in FIG. 27, the surface roughness due to the difference in the embossing of the two types of resins P1 and P2 was quantified. The illumination color temperature is 5000K.

The evaluation result of sample 3 is shown in FIG. The difference between Lab and ΔE was detected. The surface roughness index M (roughness consistency) is 85%. The three-dimensional xy match degree xy3D is 93%. With P1 as a reference, P2 had a Lab concordance of 26%, and the surface roughness could be clearly distinguished. The color difference ΔE is 1.163, ΔL is 1.532, Δa is −0.017, and Δb is 0.108. It can be seen that the L value of P2 is high and bright. The difference in material color can be seen from Δa and Δb. xy3D is a numerical value corrected for color shift.

FIG. 26 shows that the difference in “appearance” due to the difference in texture is the difference in reflection due to the difference in the surface shape of the texture. The color of the illumination is strongly reflected in the total reflection part, and the brightness is also high. Therefore, in (1), a large amount of locally totally reflected light appears to be glaring in the eyes. On the other hand, if there are few planes as in (2), the total reflection directions are dispersed and no glare is felt when viewed on the surface. In addition, various reflections are involved and color interference occurs, showing the spread of colors. Since (3) is finely diffused as a whole, the brightness is reduced. The colors are also averaged.

According to the second embodiment, an appropriate unevenness can be grasped by the surface roughness index M, and defective products can be reduced by comparing with the threshold value. For example, when the surface 5 of the resin is textured, even if there is almost no color difference ΔE, it is possible to grasp a slight change in the glossiness of the surface 5 in a deeply textured or shallowly textured one. Since it can be used in various modes such as avoiding defects in grain processing, it has high industrial utility value.

Sample 4 of Example 3 is an example of evaluating the nano-order surface roughness of the metal surface of an aluminum alloy. The surface roughness measurement value Ra (unit: nm) was measured using a non-contact roughness meter of VN-8010 of KEYENCE.

Table 3 shows the measurement / calculation results of the surface roughness index M, the surface roughness measurement value Ra, and the surface roughness evaluation index Est. The calculation in Table 3 is based on Equation 7. The three-dimensional CG showing the surface irregularities of the sample Nos. 41 to 47 of the surface roughness measurement value Ra is shown in FIGS. 29 to 35. In FIG. 29, the sample No. The average unevenness in all regions of 41 was 88.33 nm, and in FIG. 30, the sample No. The average unevenness in all regions of 42 was 58.83 nm, and in FIG. 31, the sample No. The average unevenness in all regions of 43 was 88.33 nm, and in FIG. 32, the sample No. The average unevenness in all regions of 44 was 57.71 nm, and in FIG. 33, the sample No. The average unevenness in all regions of 45 was 93.9 nm, and in FIG. 34, the sample No. The average unevenness in all regions of 46 was 112.85 nm, and in FIG. 35, the sample No. The average unevenness in all 47 regions is 100.18 nm. The above numerical value is an average value of the height from the reference surface, and is a value indicating the distortion of the surface obtained by averaging the unevenness. As the sample numbers of Sample Nos. 41 to 47 increase, the etching time is gradually increased, so that the surface roughness index M decreases and the surface roughness measurement value Ra increases.

A = 1.141439 and b = 143.1954 were obtained. The total error TotErro is 940.2347.

According to the third embodiment, an appropriate unevenness can be grasped, a nano-order surface roughness can be evaluated, and defective products can be reduced by the first calibration curve L1 shown in FIG. 37 and the third calibration curve L3 shown in FIG. it can.

Example 4 is an example of evaluating the nano-order surface roughness of the metal surface of an aluminum alloy. The surface roughness measurement value Ra (unit: μm) measures a metal roughness standard piece of a different material as a calibration substrate using a mechanical roughness meter (FORM TALYSURF (registered trademark) manufactured by Taylor Hobson). did. This is because when the original color of the metal to be measured is different from the roughness standard piece, it is necessary to correct the offset of the base color. FIG. 38 shows the measurement data.

(1) When the metal materials to be measured are the same, the surface roughness index M without offset is obtained, and the Ra value is calculated from the surface roughness index M. The calibration value is specified in JIS, and for example, the calibration curve is obtained by the least squares method.

(2) When the metal material to be measured is unknown, the surface roughness index M after the offset correction shown in the fourth embodiment is obtained and converted into a Ra value by the fourth calibration curve.

The fourth calibration curve (calibration curve) of the data shown in FIG. 38 is as shown in Equation 9.
(Number 9)
Ra = 62.31 / (M-18.526)
Ra is the measured surface roughness Ra (arithmetic mean roughness), and M is the surface roughness index.

The surface roughness measurement value Ra of the graph shown in FIG. 38 is a measurement of eight different roughness surfaces of the roughness standard piece.

Table 4 shows the Ra value calculated from the offset-calculated surface roughness index M using the 4th calibration curve, and the roughness meter by another method (Taylor Hobson, Inc., which is a mechanical roughness meter). The Ra value measured by the FORM TALYSURF (registered trademark) roughness meter manufactured by Japan Ltd. is shown. FIG. 39 is the graph and the calibration curve by the least squares method.

According to Example 4, the error of Ra was 0.75 or less, and good results were obtained.

It should be noted that the embodiments of the present invention are not limited to the above-described embodiments, and modifications and the like can be made without departing from the technical idea of the present invention. It goes without saying that things and the like are also included in the technical scope of the present invention, and various forms can be taken as long as they belong to the technical scope. For example, regarding the method of acquiring images A and B according to three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)), the method given in this embodiment is only a specific example. The technical idea of the present invention is implemented not only by these but also by other methods.

Since the surface roughness of the determination device of the present invention can be quantified by the scattering and diffraction phenomenon of the illumination light, the surface roughness can be determined very close to the human appearance determination method, and thus the surface has been dependent on the human eye. It can be applied to the evaluation of roughness. It can be applied to a wide range of materials including metals and resins, flat surfaces, curved surfaces, surfaces with complicated shapes, and various other surfaces.

1, 101, 201 ... Surface roughness determination device 2, 102, 202 ... Two-dimensional colorimeter 21 ... Photographing lenses 22a, 22b, 22c ... Optical filter 23 ... Imaging element 22a', 22c'... Dichroic mirrors 23a, 23b, 23c ... Imaging element 26 ... Reflector 27 ... Filter turret 3, 103, 203 ... Arithmetic processing unit 5, 105, 205 ... Surface 6 , 106, 206 ... Illumination unit 7 ... Display device 109 ... Switch 250 ... Roughness processing device a ... 1st coefficient b ... 2nd coefficient A, B ... Image AFM・ ・ ・ Interatomic force microscopes C1, C2 ・ ・ ・ Center coordinates D ・ ・ ・ Overlapping region ΔE ・ ・ ・ Color difference Error ・ ・ ・ Error Est ・ ・ ・ Surface roughness evaluation index G ・ ・ ・ Lattice M ・ ・ ・ Surface Roughness index H _1, H ₂・ ・ ・ Integrated number K ・ ・ ・ Inspection area T ・ ・ ・ Inspection area TotErro ・ ・ ・ Total error

Claims (2)

A 3-band visual sensitivity image with three spectral sensitivities acquired by an imager having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted to the CIE XYZ color matching function. It is equipped with an arithmetic processing unit that converts S1i, S2i, and S3i into tristimulus values X, Y, and Z in the CIE XYZ color system and calculates them.
The arithmetic processing unit
A color difference calculation unit that calculates the color difference ΔE between the inspection surface of the inspection sample and the reference surface of the reference sample,
The color space histogram distribution of the XYZ color system is obtained by dividing the inspection area of the coordinates corresponding to the color space of the XYZ color system by a grid and integrating the number of pixels of the inspection surface and the reference surface belonging to each grid. The color space histogram distribution creation part to be created and
The surface roughness index M indicating the difference between the two color space histogram distributions of the inspection surface and the reference surface is specified as the center of the two color space histogram distributions of the inspection surface and the reference surface, and one of the color space histograms is used. A surface roughness index calculation unit that calculates with or without offset correction performed by offsetting the center of the distribution so as to be close to the center of another color space histogram distribution.
A surface roughness measurement data storage unit that stores the surface roughness measurement value Ra of the inspection sample related to calibration measured by the roughness meter , and
Surface Texture evaluation index Est is, the surface roughness index M of the reference plane of the test surface and the reference sample of the test samples according to the calibration, first coefficient a according to the surface roughness index M, test samples according to the calibration It is set to be calculated based on the color difference ΔE between the inspection surface and the reference surface of the reference sample and the second coefficient b related to the color difference ΔE.
The first coefficient a and the second coefficient b are determined so that the error Error between the surface roughness measurement value Ra and the surface roughness evaluation index Est is minimized.
The first calibration curve function showing the correlation between the surface roughness measurement value Ra and the surface roughness evaluation index Est,
A second calibration curve function showing the correlation between the surface roughness measurement value Ra and the color difference ΔE,
A third calibration curve function showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated without the offset correction, or
At least one of the fourth calibration curve functions showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated with the offset correction is set , and the second to fourth calibrations are performed. The surface roughness index M and the color difference ΔE used for the line are the surface roughness index M of the inspection surface of the inspection sample related to calibration and the reference surface of the reference sample, respectively, and the inspection surface of the inspection sample related to calibration and the reference of the reference sample. The function setting unit, which is the surface color difference ΔE, and
Surface roughness index M between the inspection surface of the inspection sample and the reference surface of the reference sample related to the judgment, the color difference ΔE between the inspection surface of the inspection sample and the reference surface of the reference sample related to the judgment, and the first coefficient determined by the function setting unit. Surface roughness by a white light source, which comprises a calculation unit for calculating the surface roughness evaluation index Est'of the inspection surface of the inspection sample according to the determination based on a and the second coefficient b. Judgment device. A 3-band visual sensitivity image with three spectral sensitivities acquired by an imager having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted to the CIE XYZ color matching function. An arithmetic processing step that converts S1i, S2i, and S3i into tristimulus values X, Y, and Z in the CIE XYZ color system and calculates them.
A color difference calculation step for calculating the color difference ΔE between the inspection surface of the inspection sample and the reference surface of the reference sample, and
The color space histogram distribution of the XYZ color system is obtained by dividing the inspection area of the coordinates corresponding to the color space of the XYZ color system by a grid and integrating the number of pixels of the inspection surface and the reference surface belonging to each grid. The steps to create the color space histogram distribution to be created and
The surface roughness index M indicating the difference between the two color space histogram distributions of the inspection surface and the reference surface is specified as the center of the two color space histogram distributions of the inspection surface and the reference surface, and one of the color space histograms is used. A surface roughness index calculation step that is calculated with or without offset correction performed by offsetting the center of the distribution so as to be close to the center of another color space histogram distribution.
A surface roughness measurement data storage step for storing the surface roughness measurement value Ra of the inspection sample related to the calibration measured by the roughness meter , and
Surface Texture evaluation index Est is, the surface roughness index M of the reference plane of the test surface and the reference sample of the test samples according to the calibration, first coefficient a according to the surface roughness index M, test samples according to the calibration It is set to be calculated based on the color difference ΔE between the inspection surface and the reference surface of the reference sample and the second coefficient b related to the color difference ΔE.
The first coefficient a and the second coefficient b are determined so that the error Error between the surface roughness measurement value Ra and the surface roughness evaluation index Est is minimized, and the surface roughness measurement value Ra and the surface roughness measurement value Ra are defined. The first calibration curve function showing the correlation of the surface roughness evaluation index Est,
A second calibration curve function showing the correlation between the surface roughness measurement value Ra and the color difference ΔE,
A third calibration curve function showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated without the offset correction.
Alternatively, at least one of the fourth calibration curve functions showing the correlation between the surface roughness measurement value Ra and the surface roughness index M calculated with the offset correction is set, and the second to second. 4 The surface roughness index M and the color difference ΔE used for the calibration curve are the surface roughness index M of the inspection surface of the inspection sample related to the calibration and the reference surface of the reference sample, and the inspection surface and the reference sample of the inspection sample related to the calibration curve, respectively. Function setting step, which is the color difference ΔE of the reference surface of
Surface roughness index M between the inspection surface of the inspection sample and the reference surface of the reference sample related to the judgment, the color difference ΔE between the inspection surface of the inspection sample and the reference surface of the reference sample related to the judgment, and the first coefficient determined by the function setting step. Based on a and the second coefficient b, a calculation step for calculating the surface roughness evaluation index Est'of the inspection surface of the inspection sample according to the determination, and
A method for determining surface roughness using a white light source.

Images