Imaging Device Self-Calibration for Color Consistency
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Solution Overview
Problem
Existing imaging devices face challenges in maintaining consistent color reproduction across multiple sheets of media and different devices, as traditional calibration methods are infrequent, wasteful, and fail to correct for transient parameter variations like temperature and humidity changes.
Innovation Solution
The system continuously forms hard images on media using user-defined data, senses optical characteristics at multiple spatial locations, and calibrates the imaging device in real-time using a sensor assembly and feedback algorithm to maintain color consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If periodic color patch calibration is conducted, then color accuracy is corrected, but consumable waste increases and workflow is interrupted
Solution Approach 1:
The imaging device performs self-calibration by automatically sensing its own output and adjusting its imaging parameters without external intervention. The sensor assembly mounted on the device enables it to monitor and correct its own color accuracy, eliminating the need for manual calibration operations that waste consumables and interrupt workflow.
Solution Approach 2:
A feedback loop is established where the sensor assembly continuously monitors the optical characteristics of hard images produced by the imaging device. The sensed data is compared against target values, and correction signals are automatically sent back to adjust imaging parameters, creating a closed-loop system that maintains color accuracy without periodic manual calibration.
2Manufacturing precision
If periodic calibration is conducted, then color accuracy is corrected, but transient parameter variations during job runs are not compensated
Solution Approach 1:
The calibration process transitions from periodic discrete actions to a continuous monitoring and adjustment process. The sensor assembly operates continuously throughout the job run, constantly sensing optical characteristics and enabling real-time corrections that compensate for transient parameter variations like temperature and humidity changes.
Solution Approach 2:
The calibration system becomes dynamic rather than static. Instead of fixed periodic calibration intervals, the system continuously adapts to changing conditions by实时 monitoring optical characteristics and adjusting imaging parameters on the fly, allowing it to respond to transient variations during job runs.
3Manufacturing precision
If full color calibration is conducted frequently, then color consistency is improved, but productivity decreases due to workflow interruption
Solution Approach 1:
The imaging device performs self-calibration automatically without requiring workflow interruption. The sensor assembly and control system work together to conduct calibration operations seamlessly during normal operation, eliminating the need to stop production for manual calibration and thereby maintaining high productivity while ensuring color consistency.
Solution Approach 2:
The system uses periodic sensing actions at defined intervals during operation rather than continuous interruption. The sensor assembly takes periodic measurements of optical characteristics and makes incremental adjustments, allowing the workflow to continue while maintaining color consistency through regularly spaced calibration actions.
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
This approach provides improved color consistency across individual pages, consecutive pages, and multiple devices by continuously monitoring and adjusting optical density, reducing the need for frequent calibration and minimizing consumable waste.
Implementation Method 1
sensing an optical characteristic of at least a portion of the hard images at a plurality of different spatial locations of the hard images
Data Source
AI summary
An imaging device and calibration method therefore forms a plurality of hard images upon media using user-defined image data. An optical characteristic of at least a portion of the hard images is sensed at a plurality of different spatial locations of the hard images. The sensed optical characteristic is compared with the user-defined image data, and the imaging device is calibrated using the sensed optical characteristic.