Surface Roughness Calculation Using Scattered Light Intensity
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Solution Overview
Problem
Existing methods for calculating surface roughness using a Ward BRDF model struggle with scattered light distributions that deviate from reflected light and do not consider wavelength dependence, leading to inaccuracies in surface roughness index calculation.
Innovation Solution
A surface roughness calculation device that uses a light field camera to acquire scattered light intensity distributions, applies spatial coding and phase shift methods to calculate shape information, and corrects provisional roughness indexes until they satisfy a fitting condition, utilizing a BRDF model based on the generalized Harvey-Shack theory to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the Ward BRDF model is used to calculate surface roughness, then the calculation process is simple, but the accuracy deteriorates when scattered light distribution deviates from reflected light or wavelength dependence is not considered
Solution Approach 1:
The patent changes the fundamental parameters of the BRDF model by adopting the generalized Harvey-Shack theory instead of the Ward model. This theory incorporates wavelength-dependent parameters and scattered light distribution characteristics, transforming the calculation from a simple angle-based model to one that considers spectral properties and complex scattering behaviors, thereby resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent introduces an intermediary iterative correction process that mediates between the simplified Ward model and the complex physical reality. By using the Ward model as an initial approximation and then iteratively correcting it with scattered light distribution data and wavelength dependence considerations, the system achieves high accuracy while maintaining computational feasibility.
2Measurement precision
If the generalized Harvey-Shack theory is used to improve accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by first acquiring scattered light intensity distributions at multiple wavelengths before performing the complex Harvey-Shack calculations. This pre-processing step organizes the raw optical data into structured intensity distributions, which then serve as inputs for the theoretical model, reducing the complexity of the subsequent computational steps.
Solution Approach 2:
The patent implements feedback through iterative correction where the calculated surface roughness index is continuously refined by comparing predicted scattered light distributions with actual measurements. This feedback loop allows the system to converge to accurate results while adapting to the specific characteristics of each surface being measured, effectively managing computational complexity through adaptive refinement.
3Measurement precision
If scattered light distribution is considered in the calculation, then the accuracy for various surface types improves, but the calculation time increases
Solution Approach 1:
The patent applies partial action by selectively considering scattered light distribution characteristics that are most relevant to different surface types. Rather than fully implementing the complex Harvey-Shack theory for all cases, the system adapts the level of scattering analysis based on the specific surface being measured, achieving sufficient accuracy while reducing unnecessary computational overhead.
Solution Approach 2:
The patent introduces dynamics by making the calculation process adaptive rather than static. The system dynamically adjusts the computational approach based on the measured scattered light distribution patterns, using more sophisticated models when scattering is significant and simpler approaches when reflection dominates, thereby optimizing calculation time while maintaining accuracy across diverse surface types.
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
The device accurately calculates surface roughness without relying on the Ward BRDF model, reducing calculation errors and enabling precise measurement of surface roughness across various surface types, including curved surfaces, with high accuracy comparable to traditional methods.
Implementation Method 1
a scattered light intensity distribution acquisition unit that receives scattered light from a surface of an object and that calculates a first scattered light intensity distribution from a light reception result
Data Source
AI summary
A surface roughness calculation device includes: a scattered light intensity distribution acquisition unit that receives scattered light from a surface of an object and that calculates a first scattered light intensity distribution from a light reception result; and a surface roughness calculation unit that calculates a surface roughness index of the object, in which the surface roughness calculation unit corrects a provisional value of the surface roughness index until a second scattered light intensity distribution obtained by calculation using the provisional value of the surface roughness index and the first scattered light intensity distribution satisfy a first fitting condition, and determines the provisional value of the surface roughness index when the first fitting condition is satisfied as a value of the surface roughness index of the surface of the object.