Image Forming Apparatus Tone Calibration via Density Prediction
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Solution Overview
Problem
Current image forming apparatuses face challenges in maintaining accurate tone characteristics due to environmental variations and wear, leading to increased downtime during actual measurement calibration and lower accuracy in prediction calibration without frequent actual measurement updates.
Innovation Solution
The apparatus employs an image forming unit, sensor, and controller to form and detect pattern images of varying tone levels, using first and second calibration processes based on detection results and acquired information to generate image forming conditions, improving prediction accuracy by incorporating additional density data without the need for frequent actual measurement calibrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual measurement calibration is executed frequently to improve tone characteristic accuracy, then measurement precision is improved, but loss of time increases due to increased downtime
Solution Approach 1:
The system performs prediction calibration in advance between actual measurement calibrations to maintain tone characteristics. By predicting density values based on stored actual measurement values and current image forming conditions, the system proactively corrects tone deviations without requiring frequent full actual measurement calibrations, thus reducing downtime while maintaining accuracy.
Solution Approach 2:
The system uses feedback from actual measurement calibration results to update prediction models. The detection results from actual measurements are stored and used to refine future predictions, creating a continuous improvement loop where past measurement data enhances the accuracy of prediction calibration, allowing longer intervals between actual measurements.
2Loss of time
If prediction calibration is used to reduce downtime, then loss of time is reduced, but measurement precision deteriorates due to lower correction accuracy
Solution Approach 1:
The system introduces an intermediary prediction mechanism that bridges actual measurement calibration and tone correction. Instead of directly correcting based on limited prediction data, the system uses prediction calibration as an intermediary step that leverages stored actual measurement values and current conditions to generate accurate correction values, achieving high accuracy without full actual measurement procedures.
Solution Approach 2:
The system changes the parameters used in calibration by incorporating multiple factors into prediction calibration: stored actual measurement values, current image forming conditions, and detection results. By dynamically adjusting prediction based on these parameter changes, the system achieves high correction accuracy comparable to actual measurement calibration while avoiding the downtime associated with frequent actual measurements.
3Measurement precision
If the number of tone levels in pattern images is increased to improve calibration accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The calibration process is segmented into two distinct phases: actual measurement calibration executed periodically to establish baseline accuracy, and prediction calibration executed between measurements to maintain accuracy. This segmentation allows the system to use full-tone-pattern actual measurements less frequently while using simplified prediction-based calibration more often, reducing overall complexity without sacrificing precision.
Solution Approach 2:
The system performs partial calibration actions through prediction calibration between full actual measurements. Instead of executing complete actual measurement calibration procedures frequently, the system performs partial corrections based on predictions, using only the necessary detection results and stored data, thereby reducing the complexity and time of frequent calibration operations while maintaining sufficient accuracy.
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 enhances prediction calibration accuracy while reducing downtime by utilizing additional density data, maintaining tone characteristics at target levels without the necessity for frequent actual measurement updates.
Implementation Method 1
a sensor configured to detect a pattern image formed by the image forming unit
Data Source
AI summary
An image forming apparatus forms a first pattern image of a first number of tone levels, executes a first calibration for generating a first image forming condition based on a detection result of the first pattern image, forms a second pattern image of a second number of tone levels, acquires information having a correlation to a density of an image, and executes a second calibration for generating a second image forming condition based on the information, the detection result of the second pattern image, and the first image forming condition.