Image Forming Apparatus Calibration Timing Control
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Solution Overview
Problem
Existing image forming apparatuses face challenges in stabilizing image quality due to variations in environment and usage conditions, leading to suboptimal calibration models that are not tailored to individual usage environments, resulting in inefficiencies in calibration processes and productivity.
Innovation Solution
An image forming apparatus with a sensor and controller system that measures image density and adjusts conditions based on prediction models, allowing for real-time correction of image forming parameters to match target densities and gradations, reducing the need for frequent patch formation and improving calibration efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If calibration is performed frequently to maintain image quality, then image quality stability is improved, but productivity deteriorates due to increased downtime
Solution Approach 1:
The system performs preliminary calibration to establish baseline density values and creates prediction models in advance. These models predict future density variations based on environmental parameters and usage conditions, allowing the system to proactively adjust image forming conditions before actual density drift occurs, thereby reducing the frequency of full calibration cycles and minimizing downtime while maintaining image quality stability
2Measurement precision
If patch formation is performed frequently for calibration, then measurement accuracy is improved, but loss of time increases
Solution Approach 1:
Instead of repeatedly forming physical patch images for calibration, the system creates virtual copies of calibration data through prediction models. These models generate predicted density values based on environmental parameters and usage history, replacing the need for frequent physical patch formation while maintaining measurement accuracy through mathematical modeling of density variations
3Adaptability or versatility
If average models are used for density prediction, then adaptability to various environments is improved, but measurement precision deteriorates for individual usage conditions
Solution Approach 1:
The system transitions from using a single average prediction model to maintaining multiple prediction models tailored to specific usage environments and conditions. Each model is trained on data from its corresponding environment, allowing the system to select the most appropriate model based on current conditions. This localized approach improves prediction accuracy for individual usage scenarios while maintaining overall adaptability across diverse environments
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 enables faster and more accurate calibration, reducing downtime and improving productivity by optimizing image quality control based on real-time measurements and predictions, even in varying usage environments.
Implementation Method 1
a sensor that measures a measurement image formed by the image forming unit
Data Source
AI summary
An image forming apparatus comprises an image forming unit; a sensor that measures a measurement image; and a controller. The controller controls the image forming condition based on a measurement result of measuring a first measurement image; acquires information having a correlation to density variation of images to be formed by the image forming unit; controls the image forming condition based on the information; determines a first value regarding a density of an image to be formed, based on a measurement result of measuring a second measurement image; determines a second value regarding a density of the image to be formed by the image forming unit, based on the information; and controls a timing at which the image forming unit next forms the first measurement image, based on the first value and the second value.