Paint Color Prediction Adaptation Across Application Processes
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Solution Overview
Problem
Existing color matching and predicting methods are inaccurate due to the influence of varying paint application processes, leading to significant systematical errors and limited prediction accuracy, especially when transitioning from a reference process to a different target process.
Innovation Solution
A method and system that compensates for the influence of a target paint application process by using application adaption parameters, which are calculated to optimize color matching and prediction methods, considering factors like paint layer thickness, effect flake orientation, and tinting strength variations, based on existing sample coatings applied with the target process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a common reference paint application process is used for determining specific optical constants of colorants, then the color matching method is standardized and reproducible, but the accuracy decreases when applying to different target paint application processes
Solution Approach 1:
The patent introduces application adaption parameters that modify the physical model's optical constants based on the specific target paint application process. These parameters allow the system to adjust predictions for different application methods (spraying, brushing, rolling) without changing the fundamental reference process used for calibration, thus resolving the contradiction between standardization and accuracy for different processes
Solution Approach 2:
The patent uses application adaption parameters as intermediary factors that bridge the gap between the reference paint application process and the target process. These parameters act as mediators that translate the standardized reference data into accurate predictions for different application methods, maintaining both standardization and accuracy
2Measurement precision
If different formulations are matched for different paint application processes to achieve the same target color, then color accuracy for each process is improved, but the complexity of the color matching system increases
Solution Approach 1:
The patent creates a universal color matching system that can handle multiple paint application processes through a single integrated framework. By incorporating application adaption parameters into the physical model, the system universally applies the same matching methodology across different processes rather than requiring separate formulation matching for each process, thus reducing complexity while maintaining accuracy
Solution Approach 2:
Instead of creating separate formulation databases for different application processes, the patent uses parameter changes (application adaption parameters) to adapt the same formulation predictions to different processes. This approach maintains a single universal formulation database while achieving process-specific accuracy through parameter adjustment, reducing system complexity
3Productivity
If the physical model uses specific optical constants determined from reference application process, then the model is simplified and faster to compute, but systematic errors occur when predicting for different target application processes
Solution Approach 1:
The patent performs preliminary determination of specific optical constants using the reference paint application process, creating a pre-calibrated physical model. This preliminary action establishes a fast, accurate baseline that can be quickly adapted to different target processes by applying adaption parameters, thus maintaining computational speed while improving accuracy for different applications
Solution Approach 2:
The patent modifies the pre-calibrated physical model by applying parameter changes (application adaption parameters) that account for differences between the reference and target application processes. This allows the system to maintain the computational efficiency of the simplified physical model while correcting for systematic errors through parameter adjustment rather than requiring complete model re-calibration
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
Improves color matching accuracy by adapting to specific paint application processes, allowing for closer matches to target colors and reducing the need for iterative adjustments, thus enhancing color development and customer service efficiency.
Implementation Method 1
Physical models can predict the light reflectance properties (color) of a paint layer/paint coating based on an information about the included colorants
Implementation Method 2
The specific optical constants of colorants describe e. g. the absorption and scattering properties of colorants in the context of the physical model
Implementation Method 3
the numerical method is configured to optimize application adaption parameters by minimizing a given cost function starting from a given set of initial application adaption parameters, the given cost function being particularly chosen as a color distance between the measured color and a predicted color
Data Source
AI summary
Disclosed herein is a computer-implemented method for providing application adaption parameters to compensate for an influence of a given target paint application process on a color matching method and/or to consider an influence of a given target paint application process within a color predicting method.