Coating evaluation device and coating evaluation method

Patent metadata

Field	Value
Publication number	US-2024288375-A1
Application number	US-202118572215-A
Country	US
Kind code	A1
Filing date	Jun 21, 2021
Priority date	Jun 21, 2021
Publication date	Aug 29, 2024
Grant date	—

Abstract

In a coating evaluation device and a coating evaluation method, a coating surface is irradiated with incident light having a first intensity distribution, and a second intensity distribution of light reflected from the coating surface is acquired. Additionally, a third intensity distribution associated with the second intensity distribution is calculated based on shape information representing a curved shape of the coating surface, and an evaluation value that corresponds to the third intensity distribution is estimated by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the third intensity distribution. The curved shape of the coating surface is acquired based on measuring a coating surface or based on design data pertaining to the coating surface.

First claim

1 . A coating evaluation device comprising:
a shape acquisition unit configured to acquire shape information representing a curved shape of a coating surface based on measuring a coating surface or based on design data pertaining to the coating surface; a light source unit configured to irradiate the coating surface with incident light having a first intensity distribution; an intensity acquisition unit configured to acquire a second intensity distribution of light reflected from the coating surface; and a controller configured to calculate a third intensity distribution associated with the second intensity distribution based on the shape information, and estimate an evaluation value that corresponds to the third intensity distribution by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the third intensity distribution.

2 . The coating evaluation device according to claim 1 , wherein
a difference between the second intensity distribution and the third intensity distribution decreases commensurately with a decrease in a deviation of the curved shape from a flat shape, and the second intensity distribution and the third intensity distribution match each other when the curved shape is the flat shape.

3 . The coating evaluation device according to claim 2 , wherein
the controller is configured to calculate the third intensity distribution so as to negate a scattering component of light on the coating surface that is included in the second intensity distribution and is produced by the deviation.

4 . The coating evaluation device according to claim 1 , wherein
the evaluation model is a trained model generated through machine learning that is based on teaching data in which an intensity distribution of reflected light obtained by irradiating a flat evaluated coating surface with the incident light and the brilliance evaluation value pertaining to the evaluated coating surface are taken as a set.

5 . The coating evaluation device according to claim 1 , wherein
the brilliance evaluation value pertaining to the coating surface is an index determined according to at least one of a degree of smoothness of the coating surface, a proportion of the light reflected by the coated surface via a diffuse reflection, and a degree of resolution of an image appearing on the coating surface.

6 . The coating evaluation device according to claim 1 , wherein
the first intensity distribution has a periodic structure in a first direction.

7 . The coating evaluation device according to claim 1 , wherein
the first intensity distribution has a periodic structure in a second direction that is different from the first direction.

8 . The coating evaluation device according to claim 1 , wherein
the controller is configured to calculate a main-direction vector at a prescribed position on the coating surface based on the shape information, and is configured to set an intensity distribution that has a periodic structure in a direction of the main-direction vector as the first intensity distribution.

9 . The coating evaluation device according to claim 1 , wherein
the first intensity distribution has a first region in which an intensity of the incident light is equal to or greater than a first threshold value, and a second region in which the intensity of the incident light is less than the first threshold value and is equal to or less than a second threshold value.

10 . The coating evaluation device according to claim 1 , wherein
the shape acquisition unit is configured to acquire position information pertaining to a region of the coating surface that has been irradiated with the incident light, and is configured to acquire the shape information based on the position information.

11 . The coating evaluation device according to claim 1 , wherein
the light source unit is configured to emit the incident light, which includes a first marking pattern; the intensity acquisition unit is configured to acquire an intensity distribution of light reflected from a region of the coating surface that has been irradiated with the first marking pattern as a second marking pattern; and the controller is configured to acquire reference shape information pertaining to the region that has been irradiated with the first marking pattern based on a difference between the first marking pattern and the second marking pattern, and is configured to acquire position information pertaining to a region on the coating surface that has the shape information matching the reference shape information.

12 . The coating evaluation device according to claim 1 , further comprising
a material acquisition unit is configured to acquire material information pertaining to the coating surface, and the controller being configured to estimate the evaluation value that corresponds to a combination of the material information and the third intensity distribution by using the evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the material information and the third intensity distribution.

13 . The coating evaluation device according to claim 1 , wherein:
the evaluation model is a trained model generated through machine learning that is based on teaching data in which the material information pertaining to a flat evaluated coating surface, an intensity distribution of reflected light obtained by irradiating the evaluated coating surface with the incident light, and a brilliance evaluation value pertaining to the evaluated coating surface are taken as a set.

14 . A coating evaluation method comprising:
acquiring shape information representing a curved shape of a coating surface based on measuring the coating surface or based on data pertaining to the coating surface; irradiating the coating surface with incident light having a first intensity distribution; acquiring a second intensity distribution of light reflected from the coating surface; calculating a third intensity distribution associated with the second intensity distribution based on the shape information; and estimating an evaluation value that corresponds to the third intensity distribution by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the third intensity distribution.

15 . A non-transitory computer-readable storage medium having a coating evaluation program stored thereon, the program being executable by a computer to control
a shape acquisition unit configured to acquire shape information representing a curved shape of a coating surface based on measuring a coating surface or based on design data pertaining to the coating surface; a light source unit configured to irradiate the coating surface with incident light having a first intensity distribution; and an intensity acquisition unit configured to acquire a second intensity distribution of light reflected from the coating surface, to execute a step for acquiring the shape information by using the shape acquisition unit, a step for calculating a third intensity distribution associated with the second intensity distribution based on the shape information, and a step for estimating an evaluation value that corresponds to the third intensity distribution by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the third intensity distribution.

16 . (canceled)

Assignees

• Nissan Motor

Inventors

• Kaito Takahiro
• Yamazaki Shota
• Uehara Yoshitaka
• Yuge Kentarou
• Nagayama Mori

Classifications

• G01N2201/1296

  using neural networks · CPC title

• G01N2021/8427

  Coatings · CPC title

• G01N21/57

  Measuring gloss · CPC title

• G01N21/4738

  Diffuse reflection (precedence is given to G01N21/55 - G01N21/57 if specular component is taken into consideration), e.g. also for testing fluids, fibrous materials · CPC title

• G01N21/8422 Primary

  Investigating thin films, e.g. matrix isolation method · CPC title