Coating evaluation device and coating evaluation method

Patent metadata

Field	Value
Publication number	US-12449370-B2
Application number	US-202218572223-A
Country	US
Kind code	B2
Filing date	Jun 13, 2022
Priority date	Jun 21, 2021
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

In a coating evaluation device and a coating evaluation method, information on a coating is acquired. The information on a coating includes material information representing a material of a coating surface, and at least one of shape information representing a curved shape of the coating surface, and surface roughness information representing a surface roughness of the coating surface. An evaluation value that corresponds to a combination of the material information, the shape information, and the surface roughness information 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 material information, the shape information, and the surface roughness information.

First claim

The invention claimed is:

1. A coating evaluation device comprising:
a shape acquisition unit configured to acquire shape information representing a curved shape of a coating surface;
a surface roughness acquisition unit configured to acquire surface roughness information representing a surface roughness of the coating surface; and
a controller configured to
estimate an evaluation value that corresponds to a combination of the shape information and the surface roughness information by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the shape information and the surface roughness information.

2. 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 shape information pertaining to an evaluated coating surface, the surface roughness information pertaining to the evaluated coating surface, and the brilliance evaluation value pertaining to the evaluated coating surface.

3. The coating evaluation device according to claim 1 , wherein
the shape acquisition unit is configured to acquire design data pertaining to the coating surface as the shape information.

4. The coating evaluation device according to claim 1 , wherein
the shape acquisition unit is configured to acquire measurement data obtained by measuring the coating surface as the shape information.

5. The coating evaluation device according to claim 1 , further comprising
an image acquisition unit configured to acquire a captured image of the coating surface, and
the controller being configured to associate the captured image and the shape information with the surface roughness information and record a position on the coating surface for which the surface roughness information was acquired.

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

7. The coating evaluation device according to claim 6 , 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 an evaluated coating surface, the shape information pertaining to the evaluated coating surface, the surface roughness information pertaining to the evaluated coating surface, and the brilliance evaluation value pertaining to the evaluated coating surface.

8. A coating evaluation device comprising:
a surface roughness acquisition unit configured to acquire surface roughness information representing a surface roughness of a coating surface;
a material acquisition unit configured to acquire material information representing at least one of a type of material constituting the coating surface, a coating thickness of the coating surface, and an amount, shape, or orientation of a lustrous material included in a coating of the coating surface; and
a controller configured to
estimate an evaluation value that corresponds to a combination of the surface roughness information and the material information by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the surface roughness information and the material information.

9. The coating evaluation device according to claim 8 , wherein
the evaluation model is a trained model generated through machine learning that is based on teaching data in which the surface roughness information pertaining to an evaluated coating surface, the material information pertaining to the evaluated coating surface, and the brilliance evaluation value pertaining to the evaluated coating surface.

10. The coating evaluation device according to claim 8 , wherein
the material information includes design data that relates to at least one of a material reflectivity, a brightness, a saturation, and a hue of the coating surface.

11. The coating evaluation device according to claim 8 , wherein
the material information includes measurement data that is obtained by measuring the coating surface and that relates to at least one of a material reflectivity, a brightness, a saturation, and a hue of the coating surface.

12. The coating evaluation device according to claim 8 , further comprising
an image acquisition unit configured to acquire a captured image of the coating surface; and
the controller being configured to record, in association with the captured image, a position on the coating surface for which the surface roughness information and the material information were acquired.

13. 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 smoothness of the coating surface, a proportion of light reflected by the coating surface via diffuse reflection, and resolution of an image appearing on the coating surface.

14. A coating evaluation method comprising:
acquiring shape information representing a curved shape of a coating surface;
acquiring surface roughness information representing a surface roughness of the coating surface; and
estimating an evaluation value that corresponds to a combination of the shape information and the surface roughness information by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the shape information and the surface roughness information.

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, and
a surface roughness acquisition unit configured to acquire surface roughness information representing a surface roughness of the coating surface,
to execute
a step for acquiring the shape information by using the shape acquisition unit,
a step for acquiring the surface roughness information by using the surface roughness acquisition unit, and
a step for estimating an evaluation value that corresponds to a combination of the shape information and the surface roughness information by using an evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the shape information and the surface roughness information.

16. A non-transitory computer evaluation model disposed upon a non-transitory computer readable storage medium and configured from a neural network that includes an input layer and an output layer, the evaluation model being trained by associating
input data that is inputted to the input layer and that includes shape information representing a curved shape of a coating surface and surface roughness information representing a surface roughness of the coating surface, and
output data that is outputted from the output layer and that includes a brilliance evaluation value pertaining to the

Assignees

• Nissan Motor

Inventors

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

Classifications

• G01N2021/8893

  providing a video image and a processed signal for helping visual decision · CPC title

• G01N21/8851

  Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges (G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002) · CPC title

• G01B11/0625

  with measurement of absorption or reflection · CPC title

• G01N2021/8427

  Coatings · CPC title

• G01N2021/0125

  with stored program or instructions · CPC title