Title
Data-Driven Techniques for Printer Prognosis and Performance Improvement: Design and Critical Comparison
Author
Maffioletti, Filippo (TU Delft Mechanical, Maritime and Materials Engineering)
Contributor
Ferrari, Riccardo M.G. (mentor)
Keviczky, Tamas (graduation committee)
Kok, Manon (graduation committee)
Khalate, Amol (graduation committee)
Degree granting institution
Delft University of Technology
Programme
Mechanical Engineering | Systems and Control
Date
2019-10-30
Abstract
It is of key importance for modern printing systems to maintain high standards of efficiency, reliability and print quality. In this regard, the scope of this work is to investigate the applicability of data analysis and machine learning techniques to improve the performances of industrial printers manufactured at Océ Technologies. Two critical aspects of the considered printing process are analysed and multiple algorithms are developed. Temporary failure of printhead nozzles, a well-known issue in inkjet printing, is first addressed. Available data are used to real-time estimate the health status of each nozzle. This allows for a prompt identification of problematic scenarios and lays the foundations for the introduction of condition-based maintenance. The analysis is further enhanced by the application of machine learning. Gaussian process regression is used to predict the evolution of nozzle failures. The designed solutions show to be precious tools for nozzle prognostics, providing great accuracy and a high level of flexibility. Problematic nozzles can be restored by performing automatic cleaning actions. However, because of the costs and limited efficiency of these, their appropriateness is highly questionable. Such delicate matter is tackled by designing an autoregressive model that enables to define an advanced cleaning strategy. The presented method allows to decrease the cleaning costs up to 5-10%, leading to a considerable operational cost reduction. All the proposed solutions are thoroughly evaluated and compared, considering both their efficiency and implementation costs. Therefore they represent valuable proposals, ready to be factually implemented on an Océ printer.
Subject
Data Analysis
Prognostics
printing
Gaussian process regression
performance improvement
To reference this document use:
http://resolver.tudelft.nl/uuid:50749d7b-b4f7-4759-9e53-5c1046460e29
Embargo date
2024-10-30
Part of collection
Student theses
Document type
master thesis
Rights
© 2019 Filippo Maffioletti