Metal Surface Texture Evaluation via Optical Diffraction
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Solution Overview
Problem
Existing methods fail to accurately evaluate the finishing quality of metal surfaces, such as gloss, glaze, and roughness, relying on human visual evaluation which is time-consuming, costly, and prone to inaccuracies.
Innovation Solution
A texture evaluation apparatus using an XYZ system camera that measures reflected light distribution and diffraction patterns to quantify texture features like gloss, glaze, and irregularity, providing data close to human visual recognition through three spectral sensitivities (S1(λ), S2(λ), S3(λ)) converted to a CIE XYZ color matching function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If human visual evaluation is used to assess metal surface texture, then the evaluation can be performed with simple equipment, but the evaluation process becomes time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces the human visual evaluation system with an optical measurement system consisting of a light source, camera, and image processing unit. This substitution automates the evaluation process, eliminating the time-consuming nature of manual inspection while maintaining the ability to assess surface texture characteristics such as gloss, glaze, and irregularity.
Solution Approach 2:
The patent creates a digital copy of the metal surface appearance through photography and image processing. By capturing the reflected light distribution and diffraction patterns, the system generates an optical image that replicates the visual information a human evaluator would perceive, enabling automated analysis without losing the nuances of human visual assessment.
2Device complexity
If human visual evaluation is used to assess metal surface texture, then the evaluation method remains simple and low-cost, but the accuracy and consistency of the evaluation deteriorate
Solution Approach 1:
The patent replaces subjective human visual judgment with objective optical measurement and image processing. The system quantifies surface texture by analyzing reflected light distribution and diffraction patterns, providing consistent and accurate measurements that eliminate the variability and subjectivity inherent in human evaluation.
Solution Approach 2:
The patent transforms the evaluation from qualitative visual assessment to quantitative optical measurement. By measuring specific parameters such as reflected light intensity distribution, diffraction pattern characteristics, and color space histogram distributions, the system achieves precise and objective evaluation of surface texture properties.
3Measurement precision
If conventional roughness meters are used to measure surface irregularity, then numerical data can be obtained, but the evaluation does not closely match human visual recognition of texture quality
Solution Approach 1:
The patent creates an optical copy of the surface appearance that preserves the visual information necessary for human-like texture assessment. By capturing and analyzing the reflected light distribution and diffraction patterns, the system maintains the connection to human visual recognition while adding quantitative measurement capabilities.
Solution Approach 2:
The patent changes the measurement parameters from conventional roughness metrics to optical parameters that directly correlate with human visual perception. By analyzing reflected light intensity distribution, diffraction patterns, and color space characteristics, the system provides numerical data that accurately reflects how humans perceive surface texture and gloss.
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 evaluation of metal surface texture, significantly closer to human visual recognition, reducing time and cost while improving accuracy in determining finishing quality.
Implementation Method 1
measures reflected light distribution
Implementation Method 2
observing the light from the light source as a spread of color by diffraction according to the configuration of the metal surface
Data Source
AI summary
An object is to quantify the texture such as irregularity and gloss of a metal surface. Centers of Lab chromaticity distributions are identified (S145), and one of the Lab chromaticity distribution is entirely shifted (mapped) by deviations ΔA, ΔB and ΔL of a central coordinate, such that one of central coordinates of two distributions U1(L,a,b) and U2(L,a,b) matches with the other central coordinate (S146). A texture spread index that indicates a difference in spatial spread is then computed (S147). This configuration computes the spatial spread of the Lab chromaticity distribution in a three-dimensional space, and quantifies the irregularity of an inspection plane by diffraction phenomenon of illumination light. The difference in spread other than the color is applicable to evaluation of the irregularity of the metal surface or the like.