Image Forming System for Automated Fixing Defect Diagnosis
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Solution Overview
Problem
Existing image forming systems require manual visual inspection by experts to determine the cause of image defects such as voids and density unevenness, which is cumbersome for users.
Innovation Solution
An image forming system and method that includes an image forming unit, a first processor for inspection, and a second processor to automatically determine if a fixing defect is the cause of an image defect, using sensors to read and analyze the image and compare it with a reference image, and perform feedback control to adjust fixing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection by experts is used to determine the cause of image defects, then accurate cause identification can be achieved, but the operation becomes cumbersome and time-consuming for users
Solution Approach 1:
The image forming apparatus automatically determines the cause of image defects using its own inspection results and stored reference information, without requiring external expert intervention. The determination unit analyzes inspection data and autonomously identifies whether defects originate from the image forming unit, transfer unit, or fixing unit, enabling the system to serve itself in diagnostic functions.
Solution Approach 2:
The patent replaces manual visual inspection with an automated optical inspection system and computational analysis. Sensors capture image defects, and a determination unit processes this data against stored reference images and conditions to automatically identify defect causes, substituting human expert analysis with machine-based detection and reasoning.
2Ease of operation
If automated determination of defect causes is implemented, then ease of operation improves and manual inspection is eliminated, but the system complexity increases
Solution Approach 1:
The determination unit serves multiple functions: it identifies defect causes, determines appropriate countermeasures, and controls the discharge of defective sheets. By consolidating these diagnostic and control functions into a single integrated unit that leverages existing inspection data, the system achieves automated defect management without proportionally increasing complexity.
Solution Approach 2:
The system stores reference images and determination conditions in advance within the determination unit. These pre-stored references enable rapid automated comparison and analysis during actual defect inspection, eliminating the need for complex real-time processing algorithms and reducing the computational complexity required during operation.
3Productivity
If automated defect analysis is implemented, then productivity increases by eliminating manual inspection, but measurement precision may deteriorate without expert judgment
Solution Approach 1:
The system uses feedback from the inspection unit to inform the determination unit's analysis. Inspection results are fed back into the determination process, allowing the system to iteratively refine its defect cause identification based on actual measured data compared against stored references, maintaining high accuracy through data-driven decision making.
Solution Approach 2:
The system creates and stores reference copies of normal and defective image states during the determination conditions setting process. These reference copies serve as benchmarks for automated comparison, enabling the determination unit to accurately identify defect causes by comparing current inspection results against stored reference patterns without requiring expert intervention.
Data Source
AI summary
An image forming system includes an image forming unit that forms an image on a sheet based on printing data, a first processor that inspects the image formed by the image forming unit, and a second processor that determines whether a cause of an image defect is a fixing defect when the image defect is found by the first processor.