Image Forming Apparatus Density Prediction Model Correction
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Solution Overview
Problem
Existing image forming apparatuses face challenges in maintaining accurate density prediction models due to internal and external disturbances, leading to inconsistencies between predicted and actual density after calibration, especially when environmental conditions change.
Innovation Solution
The apparatus employs a controller to determine image forming conditions based on density information and acquires measurement data from test images to select the appropriate determination conditions, allowing for continuous correction and adaptation of the prediction model using multiple regression models and actual measurement data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a prediction model is used to omit patch formation for faster calibration, then productivity is improved, but measurement precision deteriorates due to differences between predicted and actual density
Solution Approach 1:
The system performs actual density measurements using a density detection sensor during calibration, compares the measured values with predicted values from the prediction model, and uses this feedback to correct the prediction model. This feedback mechanism allows the system to maintain high productivity by using prediction while improving accuracy through actual measurement validation and model correction.
Solution Approach 2:
The system performs preliminary density measurements at specific calibration points before full calibration, uses these preliminary results to adjust the prediction model parameters, and then proceeds with the remaining calibration steps. This preliminary action allows the system to prepare the prediction model in advance, improving overall calibration efficiency while ensuring accuracy.
2Measurement precision
If calibration is performed frequently to maintain density accuracy, then measurement precision is improved, but productivity deteriorates due to increased calibration time
Solution Approach 1:
The system performs partial calibration by measuring only specific density points (such as minimum density and maximum density points) rather than measuring all density gradations. This partial measurement approach maintains sufficient density accuracy while significantly reducing calibration time and improving productivity.
Solution Approach 2:
The prediction model serves multiple functions: it predicts density values during normal operation, guides calibration procedures, and is corrected using calibration data. This multi-functionality allows the system to maintain accuracy without requiring frequent full calibrations, thus improving productivity.
3Measurement precision
If environmental conditions are controlled to maintain prediction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system compensates for environmental variations by dynamically adjusting prediction model parameters based on measured environmental conditions (temperature, humidity). Instead of controlling the environment, the system adapts the prediction model to current environmental conditions, maintaining accuracy without adding complex environmental control mechanisms.
Solution Approach 2:
The prediction model automatically adjusts and corrects itself using data from actual density measurements taken during calibration and operation. The system self-corrects for environmental variations without requiring external environmental control, maintaining measurement precision while avoiding additional device complexity.
Data Source
AI summary
An image forming apparatus includes an image forming unit to form an image based on an image forming condition and a controller. The controller determines information related to a density of an image to be formed by the image forming unit; generates the image forming condition based on the information; acquires measurement data outputted from a sensor related to a test image; determines, based on a first determination condition, first information related to a density of the test image; determines, based on a second determination condition, second information related to a density of the test image; and selects the determination condition from among a plurality of determination conditions including the first determination condition and the second determination condition.