Printer Maintenance Timing Control via Learned Model
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Solution Overview
Problem
The variability in printhead manufacturing and environmental conditions, such as temperature and humidity, leads to inconsistent discharge characteristics in inkjet printing, necessitating a more precise timing for maintenance operations to prevent ink thickening and solidification, and ensure optimal print quality.
Innovation Solution
A system comprising a processor and memory that acquires log information from devices to learn a model estimating the necessity of maintenance operations, allowing for automatic execution at optimal timing based on job data and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If maintenance operations are performed based on fixed time intervals or simple usage conditions, then the maintenance timing can be automated, but the accuracy of maintenance timing prediction deteriorates due to variability in manufacturing and environmental conditions
Solution Approach 1:
The system collects actual maintenance data and usage information from printers, feeds this data into a learning model, and uses the learned model to predict future maintenance needs with improved accuracy. This feedback loop allows the system to adapt to variability in manufacturing and environmental conditions while maintaining automation.
Solution Approach 2:
The learning model enables the system to automatically learn from its own operation data and improve its prediction capabilities over time without external intervention. The system serves itself by using actual usage patterns and maintenance outcomes to refine future predictions, achieving both automation and precision.
2Reliability
If recovery control is performed frequently to maintain printhead condition, then print quality is maintained, but device complexity and operational overhead increase
Solution Approach 1:
The system performs preliminary prediction of maintenance needs using the learned model before actual degradation occurs. By predicting when maintenance will be necessary based on usage patterns and printhead conditions, the system can schedule recovery control operations in advance, avoiding both premature maintenance and unexpected failures.
Solution Approach 2:
The system monitors changes in printhead parameters such as discharge characteristics, ink viscosity, and usage patterns. By detecting parameter changes that indicate approaching degradation thresholds, the system can trigger recovery control only when necessary, optimizing the balance between reliability and operational simplicity.
Data Source
AI summary
According to the present invention, provided is a system comprising: an acquisition unit configured to acquire log information of a job executed on a device; and a learning unit configured to perform learning for generating, by using the log information of the device acquired by the acquisition unit as learning data, a learned model to be used by the device to estimate necessity of executing a maintenance operation.