Machine Learning for Pattern Detection in Printhead Nozzle Logging

• URL: http://arxiv.org/abs/2509.25235v1
• Date: Thu, 25 Sep 2025 07:41:10 GMT
• Title: Machine Learning for Pattern Detection in Printhead Nozzle Logging
• Abstract summary: We investigate the problem of printhead failure classification based on a multifaceted dataset of nozzle logging.<n>We propose a Machine Learning classification approach for this problem.<n>The proposed model outperformed an in-house rule-based baseline in terms of a weighted F1 score for several failure mechanisms.
• Score: 0.15749416770494704
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model [0.6156528407876973]
  This paper presents a cross-machine time-series anomaly detection framework that integrates a domain-invariant extractor with an unsupervised anomaly detection module.<n> Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines.
  arXiv  Detail & Related papers  (2026-04-07T02:16:02Z)
• Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning [24.87648457099111]
  Formulating authentication as multi-class printer classification over printer signatures lets our model capture fine-grained, device-specific features.<n>We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction.<n>Results show the framework generalises to counterfeit types unseen during training.
  arXiv  Detail & Related papers  (2026-03-09T22:33:44Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data [5.859431341476405]
  We present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from event-log data. The proposed methodology is applied to a significant real-world collected dataset. The model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.
  arXiv  Detail & Related papers  (2023-05-17T08:55:53Z)
• Deep Open-Set Recognition for Silicon Wafer Production Monitoring [7.7977112365916]
  We propose a comprehensive pipeline for wafer monitoring based on a Submanifold Sparse Convolutional Network. Our experiments show that directly processing full-resolution WDMs by Submanifold Sparse Convolutions yields superior classification performance on known classes. Our solution outperforms state-of-the-art open-set recognition solutions in detecting novelties.
  arXiv  Detail & Related papers  (2022-08-30T08:39:52Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Resolving label uncertainty with implicit posterior models [71.62113762278963]
  We propose a method for jointly inferring labels across a collection of data samples. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs.
  arXiv  Detail & Related papers  (2022-02-28T18:09:44Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis [1.0152838128195467]
  Hybrid fault diagnosis methods combining physically-based models and available training data have shown promising results. An automated residual design is developed using a bipartite graph representation of the system model to design grey-box recurrent neural networks. Data from an internal combustion engine test bench is used to illustrate the potentials of combining machine learning and model-based fault diagnosis techniques.
  arXiv  Detail & Related papers  (2020-08-11T11:59:48Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)
• Multi-label Prediction in Time Series Data using Deep Neural Networks [19.950094635430048]
  This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. The proposed algorithm is tested on two public benchmark datasets.
  arXiv  Detail & Related papers  (2020-01-27T21:35:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.