Data-Driven Techniques for Printer Prognosis and Performance Improvement

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.