Image Former Defect Diagnosis via Test Pattern Segmentation
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Solution Overview
Problem
Conventional electrophotographic image forming apparatuses face challenges in accurately identifying and addressing image defects such as negative and positive memory, which are caused by different mechanisms, leading to incomplete correction of image defects.
Innovation Solution
The apparatus generates and analyzes specific test images under varying conditions to determine the factors causing image defects, allowing for targeted recovery methods tailored to each defect type, including photoreceptor, transfer, lubricant, charging, and accumulation-type transfer memories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image defect recovery methods are used, then some image defects can be corrected, but the underlying causes are not identified leading to incomplete correction
Solution Approach 1:
The patent segments the image defect analysis into multiple test patterns (first test pattern with solid image, second test pattern with halftone image) and analyzes different density characteristics separately. This segmentation allows identification of specific defect types (negative memory, positive memory, transfer defects) by comparing density differences in different regions, thereby improving cause identification accuracy while maintaining correction effectiveness.
Solution Approach 2:
The patent introduces test patterns as intermediary objects to diagnose image defects. By forming test patterns under controlled conditions and analyzing their density characteristics, the system can identify defect causes without directly analyzing problematic production images. This intermediary approach enables precise defect classification and targeted recovery.
2Ease of operation
If generic recovery modes are applied to all image defects, then processing is simplified, but correction effectiveness decreases due to mismatched treatment
Solution Approach 1:
The patent implements a dynamic recovery approach where the recovery mode is selected based on real-time analysis of test pattern density characteristics. The system automatically determines the appropriate recovery strategy (exposure adjustment, development adjustment, transfer adjustment) based on the identified defect type, making the process adaptive rather than static. This dynamic selection maintains simplicity while improving restoration accuracy.
Solution Approach 2:
The patent employs feedback mechanisms where test pattern analysis results directly influence the selection of recovery modes. The density measurement results from test patterns provide feedback to the control unit, which then selects and executes the appropriate recovery strategy. This closed-loop feedback ensures that the recovery process is both simple to operate and highly effective.
3Measurement precision
If multiple test patterns and analysis methods are implemented, then defect identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal test pattern formation capability that can generate multiple types of test patterns (solid, halftone, different densities) using the existing image forming apparatus components. The same photoreceptor, exposure unit, and developer are used for both production imaging and test pattern formation. This multi-functionality reduces device complexity while maintaining high detection accuracy through varied test conditions.
Solution Approach 2:
The system performs self-diagnosis by automatically forming and analyzing test patterns without requiring external testing equipment or complex additional hardware. The image forming apparatus itself serves as the testing instrument, using its own components (photoreceptor, exposure unit, developer, transfer roller) to generate and measure test patterns. This self-service approach improves detection accuracy while minimizing added 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 enables precise identification of defect causes and effective recovery methods, ensuring more comprehensive correction of image defects and improved image quality.
Implementation Method 1
emitting (exposing) laser beams based on image data to a charged photoreceptor
Implementation Method 2
fixes the transferred toner image by heating with a fixer
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
An image forming apparatus (1), including: an image former (4) which includes an image carrier (41) that carries a toner image to be transferred onto a sheet, a charger (42) that charges a surface of the image carrier, an exposer (43) that performs exposure of the surface of the image carrier and a developer (44) that develops the toner image on the image carrier; a density detector (10) which detects a density of an image formed by the image former; a controller (10) which controls the image former to continuously perform image formation on a predetermined number of sheets and thereafter form a plurality of types of halftone test images on a sheet; a determiner (10) which determines whether an image defect is generated and determines a factor causing the image defect by analyzing densities of the plurality of types of test images detected by the density detector; and an executer (10) which executes a recovery mode which is set for each of the factor causing the image defect when the determiner determines that the image defect is generated, wherein the controller (10) controls the image former (4) to form a charged test image which is formed by the charger charging the surface of the image carrier and an uncharged test image which is formed without charging of the surface of the image carrier by the charger.