TLDR: This research introduces a Machine Learning approach to classify printhead failure mechanisms using nozzle logging data from Canon Production Printing. By extracting time-based and spatial features from multi-variate count time-series, an One-vs-Rest Random Forest model was developed. This model achieved an F1 score of 0.93 and outperformed an in-house rule-based baseline for several failure patterns, demonstrating the effectiveness of combining data-driven methods with domain expertise for industrial corrective maintenance.
Ensuring the quality of manufactured products is paramount for companies, and a critical aspect of this is the accurate identification of failure mechanisms. In the realm of high-volume printing, printheads developed by Canon Production Printing (CPP) are complex systems where the performance hinges on individual drop-forming nozzles. These nozzles are constantly monitored, and their failure logs can reveal distinct patterns over time and across the nozzle grid, indicating specific printhead failure mechanisms.
Traditionally, manufacturers have relied on two main approaches for classifying failures: rule-based methods and algorithmic models. Rule-based systems, often developed by domain experts, organize knowledge into a hierarchy of rules. While effective, they require manual adjustments when new data points lead to incorrect predictions. In contrast, Machine Learning (ML) models offer a data-driven approach, capable of automatically learning relationships and generalizing better to unseen scenarios.
A recent study by researchers from Maastricht University and Canon Production Printing addresses the challenge of classifying printhead failure mechanisms at their End-of-Life (EoL) stage. The paper, titled “Machine Learning for Pattern Detection in Printhead Nozzle Logging,” proposes an innovative ML-based classification framework. This framework integrates both time-series and spatial aspects of the nozzle logging data, moving beyond traditional sensor readings to analyze count time-series data – specifically, the number of component failures logged for the entire system.
The methodology adopted in this research follows a feature-based time-series classification approach. Domain experts played a crucial role in guiding the selection of a comprehensive set of time-based and spatial features. The raw nozzle log data, which can be extensive, is first sampled to consider only the first record of every print job. This data is then transformed into multi-variate time-series, where each channel represents the count information of a specific nozzle failure type. The Tsfresh Python package was utilized to extract various time-based features, capturing characteristics like linear trends, autocorrelation, and complexity. Additionally, custom features, such as first and second derivatives, the final value of the time series, and maximum differences between consecutive samples, were introduced with strong guidance from CPP domain experts. Spatial features, like the average position of failed nozzles and the number of consecutive failures from the edge of a grid, were also incorporated, resulting in a rich dataset of 430 numeric features per printhead instance.
To develop an optimal classifier, an extensive set of traditional ML models from the scikit-learn library was evaluated. After initial assessments, Random Forest (RF), Logistic Regression (LR), Extremely Randomized Trees (ET), Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were selected for further tuning. A model-based feature selection procedure was also employed to discard irrelevant features and enhance generalization. Recognizing that some printheads exhibited characteristics of multiple failure patterns (e.g., Pattern 1 and Pattern 2), the problem was framed as a multi-label classification task. To handle this, classifiers were adapted to the One-vs-Rest (OVR) framework, where a separate binary classifier is trained for each class against all others.
The evaluation, performed using a leave-one-out cross-validation (LOOCV) framework due to the imbalanced dataset, focused on precision, recall, and F1 scores, with weighted-average scores used for fair evaluation. The results showed that the OVR Random Forest model consistently outperformed other classifiers, achieving an average weighted F1 score of 0.93. This model, with 50 trees, a maximum depth of 20, and the Gini impurity criterion, was selected as the optimal classifier.
A significant finding was the comparison of the optimal ML classifier against Canon Production Printing’s in-house rule-based baseline model. While direct comparisons between rule-based and ML methods are often challenging, it was justified here as the baseline provided a strong benchmark. The ML model demonstrated clear superiority in predicting Pattern 2, Pattern 4, and Pattern 5 failure mechanisms, as reflected in higher F1 scores. For instance, the ML model achieved an F1 score of 0.94 for Pattern 2 compared to the baseline’s 0.89, and 0.91 for Pattern 5 versus 0.67. Although the baseline performed better for Pattern 3 and slightly better for Pattern 1, the ML model nearly matched its recall for Pattern 1 and showed improved overall performance with fewer misclassifications (31 incorrect predictions for ML vs. 39 for baseline).
The study also provided valuable insights into feature importance. Analysis of the Gini index for the Random Forest model revealed that custom features, particularly those derived from domain knowledge such as the number of consecutive NF4s, maximum differences, and derivatives, were highly relevant for predicting Pattern 1, Pattern 2, and Pattern 4 classes. This highlights the crucial synergy between data-driven approaches and expert domain knowledge in developing effective industrial maintenance solutions.
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In conclusion, this research successfully developed an ML classifier that effectively addresses the complex problem of classifying printhead failure mechanisms from nozzle logging data. By transforming multi-variate count time-series into fixed-length feature vectors, the OVR Random Forest model achieved human-level performance, outperforming the rule-based baseline for several critical failure patterns. This framework offers a promising approach for analyzing similar systems composed of multiple small parts prone to degradation over time, underscoring the impact of predictive maintenance on industrial quality assurance. For more details, you can read the full paper here.
