Sheet Classification Using Passage Interval Data
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Solution Overview
Problem
Image forming apparatuses, such as printers and copiers, often malfunction due to incorrect paper type information, leading to sheet collisions and operational issues like jams and printing problems, which are difficult to resolve.
Innovation Solution
A system that uses a machine learning model to classify sheets based on sheet passage interval data, adjusting operational parameters like image quality and toner usage, and modifies the number of sheets processed to optimize conveyance, utilizing a sheet sensor and controller to detect anomalies and transmit data for external evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually input paper type information, then the image forming apparatus can adjust operational parameters, but users may forget to enter information or fail to change information when paper is changed, leading to incorrect classification
Solution Approach 1:
The system automatically detects and classifies paper types using machine learning models that analyze sheet passage interval data from sensors, eliminating the need for users to manually input paper type information. The apparatus serves itself by autonomously identifying paper characteristics and adjusting operational parameters accordingly.
Solution Approach 2:
The manual input mechanism is replaced with an automated sensor-based detection system. Optical sensors and machine learning algorithms substitute for user interaction, analyzing sheet passage intervals to infer paper type characteristics without requiring physical user input.
2Productivity
If the system processes a large number of sheets continuously, then productivity increases, but sheet collisions and operational issues like jams occur more frequently
Solution Approach 1:
The system continuously monitors sheet passage intervals using sensors and compares actual intervals against expected intervals for the detected paper type. When anomalies are detected that indicate potential collisions or jams, the system provides feedback to adjust conveyance parameters or alert operators, enabling continuous operation while maintaining reliability.
Solution Approach 2:
The sheet conveyance system dynamically adjusts operational parameters based on real-time paper type classification. Conveyance speed, acceleration, and other parameters are optimized for each detected paper type, allowing high-speed processing of appropriate paper types while preventing collisions through adaptive control.
3Adaptability or versatility
If the system uses a fixed number of sheets for classification windows, then processing is simple, but it cannot adapt to different paper types with varying optimal window sizes
Solution Approach 1:
The classification system dynamically adjusts the number of sheets in classification windows based on the detected paper type. Different paper types have different optimal window sizes, and the system adapts these parameters in real-time to maximize classification accuracy for each paper type while managing complexity through automated parameter adjustment.
Data Source
AI summary
A method comprises obtaining a training dataset, the training dataset comprising sheet passage interval information associated with conveyance of a plurality of sheets in a sheet conveyance path of a training sheet processing apparatus, at least a portion of the sheets of the plurality of sheets having a known sheet classification of a set of sheet classifications; obtaining sheet timing data associated with the classifying, the sheet timing data comprising sheet passage interval information associated with conveyance of a predetermined number of sheets in the sheet conveyance path of a deployed sheet processing apparatus; classifying, using a machine learning model and the training dataset and the sheet timing data, for each interval window of a plurality of different interval windows, a predetermined number of sheets in a sheet conveyance path of a deployed sheet processing apparatus as a particular classification of the set of sheet classifications; and adjusting, based on the classifying, one or more operational parameters of a deployed sheet processing apparatus.