Repair Paint Texture Matching via Digital Imaging Analysis
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Solution Overview
Problem
Current methods for matching repair paints to the texture properties of original paint films are inefficient and rely heavily on human judgment, often resulting in unsatisfactory matches due to the difficulty in selecting the right effect modules and the potential disruption of texture during color adjustments.
Innovation Solution
A method that formulates repair paints using paint modules associated with specified texture data, employing a calculational texture model to calculate a matching mixture of toners, which can be optimized to achieve both color and texture matches, utilizing digital imaging and image analysis software to extract texture parameters like coarseness and glints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual judgment by eye is used to match texture, then the process is simple, but the accuracy and reliability of the match deteriorates due to strong dependence on practitioner skills
Solution Approach 1:
The patent replaces the human visual judgment system with an automated digital imaging and image analysis system. A camera captures images of the paint film, and software algorithms objectively analyze texture parameters such as coarseness, glints, and micro-brilliance, eliminating dependence on practitioner skills while maintaining operational simplicity.
Solution Approach 2:
The patent introduces digital imaging devices and image analysis software as intermediaries between the paint film and the matching decision. These tools serve as a mediator that translates visual texture properties into quantifiable data, enabling both simple operation and accurate measurement simultaneously.
2Adaptability or versatility
If effect modules are selected through trial and error to obtain matching texture, then flexibility in selection is maintained, but the time and complexity of the formulation process increases
Solution Approach 1:
The patent performs preliminary digital imaging and analysis of the original paint film's texture before formulation begins. By pre-determining the target texture parameters through objective measurement, the system eliminates the need for trial-and-error selection of effect modules, significantly reducing formulation time while maintaining the ability to adapt to different paint types.
Solution Approach 2:
The patent implements a feedback loop where the measured texture parameters of the original paint film are used to guide the selection and optimization of effect modules in the repair paint formulation. This closed-loop approach ensures both flexibility in achieving the desired texture and efficiency by avoiding unnecessary iterations.
3Measurement precision
If color formulas are iteratively adjusted to achieve color match, then color accuracy is improved, but the texture match may be disrupted and requires re-adjustment
Solution Approach 1:
The patent merges the color matching and texture matching processes into a single integrated formulation approach. By simultaneously considering both colorimetric data and texture parameters from the beginning, the system achieves color accuracy without disrupting texture, as both properties are optimized together rather than sequentially.
Solution Approach 2:
The patent performs preliminary measurement and analysis of both color and texture properties of the original paint film before formulation. This pre-characterization allows the formulation system to simultaneously target both color and texture matches from the start, preventing the need for iterative adjustments that could disrupt either property.
Data Source
AI summary
A method for matching colour properties and texture properties of a repair paint to colour properties and texture properties of a paint film on a substrate to be repaired is provided. In the method, the texture of the paint film is imaged with a digital imaging device, the imaged texture is analyzed using image analysis software, texture data is calculated, and the repair paint is formulated on the basis of the concentrations of paint modules, wherein each paint module is associated to specified texture data and colour data.