Color Batch Correction with Spectral Coordinates and Machine Learning
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Solution Overview
Problem
Modern coatings often fail to meet accuracy thresholds due to environmental and process variations, requiring lengthy iterative adjustments that can lead to wasted production batches.
Innovation Solution
A system utilizing machine learning algorithms to analyze spectral data of coated panels, converting it into three-dimensional color coordinates, and determining adjustments to coatings based on linear regressions to achieve target color matches efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional iterative formula adjustments are used to correct coating color batches, then color accuracy can be improved, but production time increases significantly and multiple iterations are required
Solution Approach 1:
The system performs preliminary analysis by converting spectral data into three-dimensional color coordinates (L*, a*, b*) and using machine learning algorithms to predict the required adjustments before actual coating correction. This preliminary computational action determines the optimal adjustment amounts in advance, eliminating the need for multiple iterative adjustments and significantly reducing production time while maintaining color accuracy.
2Manufacturing precision
If multiple iterations of color correction are performed, then color matching accuracy improves, but material waste increases due to ruined production batches
Solution Approach 1:
The machine learning algorithm enables the system to self-determine the optimal adjustments by analyzing spectral data and predicting the precise amount of colorant needed to achieve target color matching. This self-service capability eliminates the need for human operators to perform multiple trial-and-error adjustments, thereby preventing material waste from ruined production batches while ensuring accurate color matching.
3Manufacturing precision
If manual formula adjustments are made repeatedly, then color specifications can be met, but labor intensity and operational complexity increase
Solution Approach 1:
The system replaces manual mechanical adjustment operations with an automated computational system. Spectral data is automatically converted into three-dimensional color coordinates, and machine learning algorithms automatically determine the optimal adjustments. This substitution of mechanical/manual operations with automated intelligent systems maintains color specification compliance while dramatically improving operational simplicity and reducing labor intensity.
Data Source
AI summary
A system for performing color batch correction may include processors and computer-readable media having stored thereon executable instructions. In some examples, the executable instructions, if executed by the processors, cause the system to receive spectral data corresponding to a set of panels and convert the spectral for reach coated panel into a set of three-dimensional coordinates (e.g., lightness, red/green, and blue/yellow values). The system determines a change in each coordinate for each coated panel and analyze the change using a machine learning algorithm. The system receives data associated with a first coating and a target coating and determine one or more adjustments to make to the first coating to reduce a delta value between the coatings based on applying the machine learning algorithm. The system then outputs an indication of the one or more adjustments and a predicted reduction of the delta value.