Coating evaluation device and coating evaluation method
AssigneeNissan Motor Co., Ltd.
InventorsShota Yamazaki, Takahiro Kaito, Kentarou Yuge, Mori Nagayama, Yoshitaka Uehara
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Abstract
In a coating evaluation device and a coating evaluation method, information on a coating is acquired. The information on a coating includes shape information representing a curved shape of a coating surface and surface roughness information representing a surface roughness of the coating surface. An evaluation value that corresponds to a combination of 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 shape information and the surface roughness information.
Claims13
A coating evaluation device comprising:
- a shape acquisition unit including a 3D scanner, the shape acquisition unit being configured to acquire shape information representing a curved shape of a coating surface;
- a surface roughness acquisition unit including a laser microscope, the surface roughness acquisition unit being configured to acquire surface roughness information representing a surface roughness of the coating surface;
- and
- a controller including a processor, the controller being 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.
A coating evaluation method comprising:
- using a controller including a processor to execute;
- 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.
A non-transitory computer-readable storage medium having a coating evaluation program stored thereon, the program being executable by a computer that includes a processor, the processor being configured to control
- a shape acquisition unit that includes a 3D scanner and is configured to acquire shape information representing a curved shape of a coating surface, and
- a surface roughness acquisition unit that includes a laser microscope and is 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.
A non-transitory computer-readable storage medium having a computer evaluation model stored thereon, the computer evaluation model being 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 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 includes a brilliance evaluation value pertaining to the coating surface.
An evaluation model generation method comprising:
- using a controller including a processor to execute;
- acquiring teaching data in which shape information representing a curved shape of a coating surface, surface roughness information representing a surface roughness of the coating surface, and a brilliance evaluation value pertaining to the coating surface;
- and
- performing machine learning that is based on the teaching data to generate an evaluation model that outputs the brilliance evaluation value pertaining to the coating surface in response to an input including the shape information and the surface roughness information.
Dependencies are inferred from the claim text.