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Machine Learning for Pattern Detection in Printhead Nozzle Logging

Nikola Prianikov, Evelyne Janssen-van Dam, Marcin Pietrasik, Charalampos S. Kouzinopoulos

TL;DR

This work tackles classifying printhead failure mechanisms from nozzle-logging data by casting it as a multivariate count time-series problem with spatial nozzle-grid features. It introduces a feature-based time-series pipeline that converts logs into fixed-length vectors using Tsfresh-derived features plus domain-specific metrics, then trains an OvR Random Forest on 430 features. The RF approach achieves a weighted F1 around $0.93$ and outperforms the in-house rule-based baseline on several patterns, illustrating the benefit of combining domain knowledge with data-driven methods for industrial corrective maintenance. The methodology generalizes to other multi-component systems where pattern-based degradation evolves over time, offering practical impact for reliability engineering and maintenance planning.

Abstract

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.

Machine Learning for Pattern Detection in Printhead Nozzle Logging

TL;DR

This work tackles classifying printhead failure mechanisms from nozzle-logging data by casting it as a multivariate count time-series problem with spatial nozzle-grid features. It introduces a feature-based time-series pipeline that converts logs into fixed-length vectors using Tsfresh-derived features plus domain-specific metrics, then trains an OvR Random Forest on 430 features. The RF approach achieves a weighted F1 around and outperforms the in-house rule-based baseline on several patterns, illustrating the benefit of combining domain knowledge with data-driven methods for industrial corrective maintenance. The methodology generalizes to other multi-component systems where pattern-based degradation evolves over time, offering practical impact for reliability engineering and maintenance planning.

Abstract

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.

Paper Structure

This paper contains 16 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Nozzle logging and failure mechanisms of printers
  5. Dataset
  6. Model design
  7. Data extraction and representation
  8. Development of an optimal classifier
  9. Evaluation
  10. Evaluation procedure and metrics
  11. Results
  12. Selection of the highest performing ML model
  13. Evaluation of an optimal ML classifier against the rule-based baseline
  14. Feature importance in class predictions
  15. Discussion
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Example of a pattern in the nozzle log.
  • Figure 2: Overview of the modeling framework.
  • Figure 3: Confusion matrices of prediction results of the rule-based baseline model and proposed ML model across common output labels.