Coating Color Matching with Radiative-Transfer-Constrained ML
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Solution Overview
Problem
Existing color matching techniques for coatings are inefficient, time-consuming, or inaccurate, particularly in determining chemical compositions to match existing coatings.
Innovation Solution
A system utilizing a physics-informed machine learning algorithm with a radiative transfer function as a regularization agent to process color measurements from a spectrophotometer, generating a set of color components that correspond to the target coating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color matching techniques are used, then the process can be performed with simple equipment, but the matching accuracy and efficiency deteriorate
Solution Approach 1:
The patent introduces a physics-informed machine learning model as an intermediary between the spectrophotometer measurement and the color component determination. This model acts as a mediator that processes the measured color data through physics-based radiative transfer equations to accurately predict the coating composition, thereby improving measurement precision without requiring direct complex physical measurement of each color component.
Solution Approach 2:
The patent transforms the color matching problem from directly measuring physical coating properties to analyzing spectral data in the wavelength domain. By converting the problem into parameter space transformations (from reflectance spectra to color component concentrations through physics-informed algorithms), the system achieves higher precision while managing complexity through mathematical parameter transformations rather than physical measurement complexity.
2Productivity
If traditional iterative color matching methods are used, then the process can be performed with simple algorithms, but the time consumption increases
Solution Approach 1:
The patent pre-trains the physics-informed machine learning model with extensive spectral data and radiative transfer physics before actual color matching. This preliminary action allows the model to learn the complex relationships between color components and spectral characteristics in advance, so that during actual operation, the matching process requires only forward propagation through the trained model rather than iterative trial-and-error, dramatically reducing matching time while maintaining high efficiency.
3Measurement precision
If physics-informed machine learning algorithms are used, then the color matching accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent replaces complex iterative mechanical/mathematical calculation systems with a pre-trained physics-informed machine learning model. Instead of solving radiative transfer equations iteratively during each color matching operation, the system uses the trained neural network to directly predict color components from spectral data, substituting the computational mechanics with a more efficient inference process that maintains accuracy while reducing real-time computational complexity.
4Manufacturing precision
If conventional color matching processes are used, then the process flow remains simple, but the determination of chemical composition becomes inaccurate
Solution Approach 1:
The patent segments the color matching process into distinct functional modules: spectral data acquisition, physics-informed feature extraction, machine learning-based composition prediction, and validation. By dividing the complex task of determining chemical composition into these segmented stages, each handled by specialized processing components, the system achieves higher manufacturing precision for coating composition while managing overall system complexity through modular architecture.
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 more efficient, accurate, and less time-consuming color matching processes by leveraging physics-informed machine learning to determine optimal coating compositions.
Implementation Method 1
receive, from a spectrophotometer, a color measurement of a first coating, wherein the color measurement corresponds with a first color curve
Implementation Method 2
a radiative transfer function acts as a regularization agent that limits a space of admissible solutions within the physics-informed machine learning algorithm
Data Source
AI summary
A system may receive, from a spectrophotometer. a color measurement of a first coating, wherein the color measurement corresponds with a first color curve. Additionally, the system may process, with a physics-informed machine learning algorithm, information derived from the first color curve, wherein a radiative transfer function acts as a regularization agent that limits a space of admissible solutions within the physics-informed machine learning algorithm. The system may also generate, from an output of the physics-informed machine learning algorithm, a set of one or more color components that, if mixed, correspond to the first color curve.