White Light Surface Roughness Determination via Color Histogram Analysis
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Solution Overview
Problem
Conventional surface roughness determination techniques are inefficient and inaccurate, particularly for measuring small irregularities at the nano-level and micron-level, and are unable to effectively assess the surface roughness of curved or large areas, leading to bottlenecks in production line processes.
Innovation Solution
A surface roughness determination apparatus using a white light source and an XYZ-system imaging device that measures reflected light distribution, quantifies surface roughness through scattering and diffraction, and employs calibration curves to minimize errors, allowing for accurate and efficient evaluation of surface roughness indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional roughness meters (mechanical or optical) are used to measure surface roughness, then measurement can be performed, but measurement time is long and device scale is large
Solution Approach 1:
The patent replaces mechanical contact measurement methods with optical measurement using a camera and light source. The system captures reflected light images from the surface and analyzes intensity variations to determine roughness, eliminating the need for mechanical contact and significantly reducing measurement time while maintaining accuracy for small irregularities at nano-level and micron-level
Solution Approach 2:
The patent creates an optical copy of the surface by capturing reflected light patterns with a camera. Instead of physically contacting the surface, the system creates a light-based representation of surface irregularities and analyzes this optical copy to determine roughness characteristics, enabling rapid non-contact measurement
2Measurement precision
If laser beam scanning is used to measure surface roughness, then measurement can be performed, but measurement time is long
Solution Approach 1:
The patent uses periodic illumination with a specific wavelength light source that matches the regularity of surface irregularities. By using light with wavelength corresponding to the periodicity of surface features, the system enhances the contrast of reflected light patterns, enabling faster capture and analysis of roughness characteristics without requiring time-consuming scanning procedures
Solution Approach 2:
The patent transitions from one-dimensional line-by-line laser scanning to two-dimensional simultaneous imaging using a camera sensor. The entire surface area is captured in a single frame, allowing parallel processing of multiple measurement points and dramatically reducing measurement time while maintaining precision for curved and large surfaces
3Productivity
If visible light or simple optical methods are used, then measurement can be performed, but accuracy is insufficient for small irregularities
Solution Approach 1:
The patent changes the wavelength parameter of the light source to match the regularity and scale of surface irregularities being measured. By selecting light wavelengths that correspond to the periodicity of surface features (nano-level and micron-level), the system maximizes the interaction between light and surface roughness, producing enhanced reflected light patterns that provide accurate measurement data for small irregularities
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient determination of surface roughness, significantly reducing measurement time and improving the assessment of curved and large surfaces, aligning with human visual determination standards.
Implementation Method 1
measures a reflected light distribution from the surface by scattering and diffraction of the light from the light source
Implementation Method 2
measures a reflected light distribution from the surface by scattering and diffraction of the light from the light source
Data Source
AI summary
Object:An object is to quantify the roughness of a test surface by scattering and diffraction of illumination light and to evaluate the matching degree of surface roughness separately from color based on a difference in roughness.Solution to ProblemA surface roughness determination apparatus 1 using a white light source includes an arithmetic processing unit 3 configured to convert 3-band visual sensitivity images S1i, S2i and S3i, which respectively have three spectral sensitivities (S1(λ), S2(λ) and S3(λ)) subjected to linear transformation so as to be equivalent to a CIE XYZ color matching function and are obtained from a surface 5 by a two-dimensional colorimeter 2 using the three spectral sensitivities (S1(λ), S2(λ) and S3(λ)), into tristimulus values X, Y and Z in a CIE XYZ color system and perform arithmetic operations. This arithmetic processing unit 3 includes a color difference calculator configured to calculate a color difference ΔE; a color space histogram distribution creator configured to divide an examination area of coordinates corresponding to a color space in the XYZ color system by grids G and respectively integrate the numbers of pixels on a test surface and on a reference surface included in each of the grids G, so as to create color space histogram distributions in the XYZ color system; a surface roughness index calculator configured to calculate a surface roughness index M indicating a difference between the two color space histogram distributions of the test surface and the reference surface with or without an offset correction; a surface roughness measurement data storage unit configured to store a measured surface roughness value Ra actually measured by a roughness meter; and a function setter configured to set at least one of a first calibration curve function L1 indicating a correlation of the measured surface roughness value Ra to a surface roughness evaluation index Est, a second calibration curve function L2 indicating a correlation of the measured surface roughness value Ra to the color difference ΔE, a third calibration curve function L3 indicating a correlation of the measured surface roughness value Ra to the surface roughness index M calculated without the offset correction and a fourth calibration curve function indicating a correlation of the measured surface roughness value Ra to the surface roughness index M calculated with the offset correction.