Machine Learning for Pattern Detection in Printhead Nozzle Logging
This work addresses failure detection for printhead manufacturers, but it is incremental as it applies existing time-series classification methods to a specific domain.
The paper tackled the problem of classifying printhead failures using nozzle logging data, proposing a machine learning approach that outperformed an in-house rule-based baseline with improved weighted F1 scores for several failure mechanisms.
Correct identification of failure mechanisms is essential for manufacturers to ensure the quality of their products. Certain failures of printheads developed by Canon Production Printing can be identified from the behavior of individual nozzles, the states of which are constantly recorded and can form distinct patterns in terms of the number of failed nozzles over time, and in space in the nozzle grid. In our work, we investigate the problem of printhead failure classification based on a multifaceted dataset of nozzle logging and propose a Machine Learning classification approach for this problem. We follow the feature-based framework of time-series classification, where a set of time-based and spatial features was selected with the guidance of domain experts. Several traditional ML classifiers were evaluated, and the One-vs-Rest Random Forest was found to have the best performance. The proposed model outperformed an in-house rule-based baseline in terms of a weighted F1 score for several failure mechanisms.