Image Density Calibration via Predictive and Sensor Feedback
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Solution Overview
Problem
Predictive calibration in image forming apparatuses faces decreased accuracy when input parameter values significantly differ from those used to generate the prediction model, leading to reduced calibration accuracy.
Innovation Solution
An image forming apparatus with a prediction unit to predict image density, a sensor to measure images, and a controller to execute calibration based on environmental conditions, allowing for both predictive and measurement-based calibrations to maintain image density accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive calibration is performed without forming a tone pattern, then downtime is reduced and productivity is improved, but calibration accuracy deteriorates when parameter values differ significantly from those used to generate the prediction model
Solution Approach 1:
The system uses measured density values from the sensor as feedback to update and refine the prediction model. The controller compares predicted density values with actual measured values, and uses this feedback to adjust the prediction model parameters, thereby improving calibration accuracy while maintaining the efficiency of predictive calibration.
Solution Approach 2:
The system performs preliminary calibration by forming a tone pattern on the intermediate transfer body and measuring it to generate initial prediction model parameters. This preliminary action establishes a baseline that enables subsequent predictive calibration to be performed quickly without repeated tone pattern formation, while still maintaining accuracy through the established model.
2Measurement precision
If a tone pattern is formed on a sheet for calibration, then calibration accuracy is improved through direct measurement, but downtime increases and productivity decreases
Solution Approach 1:
The system uses the intermediate transfer body as an intermediary medium for calibration measurements instead of forming tone patterns on final sheets. This allows calibration to be performed on a reusable component that can be measured multiple times without consuming sheets, thereby improving productivity while maintaining calibration accuracy through direct measurement capability.
Solution Approach 2:
The system creates a copy of the calibration process by forming tone patterns on the intermediate transfer body rather than on final output sheets. This copying approach allows calibration to be performed separately from production, enabling accurate measurements without impacting sheet production throughput and reducing downtime.
3Device complexity
If the prediction model is generated based on limited parameter values, then model complexity is reduced, but adaptability deteriorates when environmental conditions change
Solution Approach 1:
The system implements dynamic adaptation by allowing the prediction model parameters to be updated based on environmental condition detection. When environmental conditions change beyond predetermined ranges, the controller detects this and triggers model parameter updates, making the otherwise simple model adaptable to new conditions without increasing its fundamental complexity.
Solution Approach 2:
The system changes model parameters dynamically based on environmental conditions. The prediction model uses different parameter sets depending on detected environmental ranges, allowing the same model structure to adapt to varying conditions. This approach maintains model simplicity while achieving environmental adaptability through parameter adjustment rather than structural complexity.
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 calibration accuracy by dynamically adjusting image forming conditions based on real-time measurements and predictions, minimizing downtime and maintaining consistent image density despite environmental changes.
Implementation Method 1
a sensor configured to measure a measurement image on the image carrier formed by the image forming unit
Data Source
AI summary
An image forming apparatus includes: an image forming unit configured to form an image on a sheet based on an image forming condition; an image carrier; a sensor configured to measure a measurement image on the image carrier; a prediction unit configured to predict a density of an image to be formed; a detector configured to detect an environmental condition; and a controller. The controller is configured to: control whether to execute first calibration, in which the measurement image is not formed and the image forming condition is controlled based on the density predicted by the prediction unit, based on the environmental condition detected by the detector; and execute second calibration, in which the image forming condition is controlled based on a measurement result of the measurement image measured by the sensor.