Printhead Maintenance with Anomaly Scoring for Replacement
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Solution Overview
Problem
Printheads in printing apparatuses often suffer from clogging, structural failures, and other defects, leading to degraded print quality, necessitating timely and accurate identification and replacement to avoid unnecessary costs.
Innovation Solution
A printhead maintenance supervisor utilizing machine learning, specifically neural networks, to monitor printhead performance, generate anomaly scores, and recommend replacement based on scaled anomaly scores, thereby improving the accuracy of replacement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If printhead replacement is performed based on traditional monitoring methods, then maintenance simplicity is maintained, but replacement accuracy deteriorates leading to unnecessary replacements
Solution Approach 1:
The monitoring system is segmented into multiple independent neural networks: a first neural network for generating initial anomaly scores from conforming printhead data, and a second neural network for generating scaled anomaly scores from training printhead data. This segmentation allows each network to specialize in specific tasks, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary training datasets and intermediate anomaly scores as mediators between the raw printhead data and the final replacement recommendations. The first neural network generates intermediate anomaly scores that are then used to create training datasets for the second neural network, which produces the final scaled anomaly scores. This intermediary layer enables progressive refinement of detection accuracy.
2Reliability
If printhead replacement is delayed to avoid unnecessary costs, then cost reduction is achieved, but print quality deteriorates due to undetected failures
Solution Approach 1:
The system implements feedback through continuous monitoring of printhead performance data and generation of anomaly scores that provide feedback on printhead health status. The neural networks analyze patterns in the data and provide scaled anomaly scores that indicate the likelihood of failure, enabling timely replacement decisions that maintain print quality while avoiding unnecessary replacements of healthy printheads.
Solution Approach 2:
The patent transforms raw printhead data into scaled anomaly scores through the neural network processing, changing the parameter representation from raw measurements to normalized reliability indicators. This parameter transformation allows for more accurate assessment of printhead condition and enables better decision-making between replacement and retention.
3Measurement precision
If traditional monitoring methods are used, then system simplicity is maintained, but detection precision of printhead failures deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the neural networks on extensive datasets of conforming and training printhead data before actual failure detection is needed. The first neural network is trained on conforming printhead data to generate baseline anomaly scores, and the second neural network is trained on training printhead data to generate scaled anomaly scores. This preliminary training enables high-precision failure detection when the systems are deployed.
Data Source
AI summary
Systems and methods of recommending replacement of printheads. In an embodiment, a system trains a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads. The system generates a training dataset for a recurrent second neural network by identifying training printhead data for a pool of training printheads, inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a plurality of time units, and formatting third training samples for the training printheads. The system trains the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the second training dataset.