[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122499-en":3,"doc-seo-122499-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122499,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning for Pattern Detection in Printhead Nozzle Logging","Correct identification of printhead failure mechanisms is crucial for manufacturers to maintain product quality and effective corrective maintenance. Using constantly recorded nozzle logging, failures can be expressed as time-evolving and spatial patterns over the nozzle grid, including the number of failed nozzles in time and their distribution in space. This work presents an ML classification approach within a feature-based time-series framework, selecting time and spatial features with domain expertise.","Machine Learning for Pattern Detection in Printhead Nozzle Logging  \nCitation for published version (APA):  \nPrianikov, N. , Dam, E. J. , Pietrasik, M. , & Kouzinopoulos, C. (2025) . Machine Learning for Pattern Detection in Printhead Nozzle Logging.  \nDocument status and date:  \nPublished: 25/09/2025  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.umlib.nl/taverne-license](www.umlib.nl/taverne-license)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 11 Jan. 2026  \nMachine Learning for Pattern Detection in Printhead Nozzle Logging  \nNikola Prianikov, Marcin Pietrasik, Charalampos S. Kouzinopoulos  \nDepartment of Advanced Computing Sciences Maastricht University  \nMaastricht, The Netherlands  \nEvelyne Janssen  \nQuality Processes and Validation Department Canon Production Printing Venlo, The Netherlands  \narXiv :2509 .25235v1 [ cs .LG] 25 Sep 2025  \nAbstract—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-vsRest 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.  \nIndex Terms—feature engineering, time-series classification, corrective maintenance, printing, nozzle log  \nI. INTRODUCTION  \nIdentifying failure mechanisms is a critical part of industrial corrective maintenance for manufacturers to ensure the quality of their products [1] . Many manufactured systems are made up of smaller components that can fail over time, eventually causing entire system breakdown. ","cbCaibWsjjjbCUcA","https://ap.wps.com/l/cbCaibWsjjjbCUcA","pdf",667147,1,6,"English","en",105,"# Introduction\n## Failure mechanism identification and corrective maintenance\n## Rule-based vs algorithmic machine learning approaches\n## Industrial end-of-life printhead classification challenge","[{\"question\":\"Why is identifying printhead failure mechanisms important?\",\"answer\":\"Accurate identification supports industrial corrective maintenance and helps manufacturers ensure product quality by linking observed behavior to likely failure causes.\"},{\"question\":\"How are nozzle failures represented in the proposed work?\",\"answer\":\"Failures are derived from continuously recorded nozzle states and expressed as distinct time-based and spatial patterns across the nozzle grid.\"},{\"question\":\"Which machine learning model performed best and what did it improve?\",\"answer\":\"The One-vs-Rest Random Forest achieved the best results, outperforming an in-house rule-based baseline on weighted F1 score across multiple failure mechanisms.\"}]","Machine Learning for Pattern Detection in Printhead Nozzle Logging | 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