Multi-resolution screening of paper formation variations on production line

Ejnarsson, Marcus; Verikas, Antanas; Nilsson, Carl-Magnus

doi:10.1016/j.eswa.2008.01.043

Cited by 5 publications

(3 citation statements)

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“…The model exploiting the same four quality attributes: 6,8,13, and 16, as in the ''average subject'' model, provided the highest print quality score prediction accuracy. All the four attributes were correctly identified already by the model exploiting all 25 variables, see Fig.…”

Section: Predicting a Print Quality Score From Print Quality Attributesmentioning

confidence: 93%

“…A large variety of deficiencies and problems may appear during printing: misregistration of printing plates, paper wrinkle formation [5], poorly formed paper surface [6], too high linting due to poorly formed paper surface [7], piling, mottling, web breaks, etc. Even if all the aforementioned deficiencies are absent, the quality of print can be low.…”

Section: Introductionmentioning

confidence: 99%

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Assessing print quality by machine in offset colour printing

Lundström

Verikas

2013

Knowledge-Based Systems

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Section: Predicting a Print Quality Score From Print Quality Attributesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Assessing print quality by machine in offset colour printing

Lundström

Verikas

2013

Knowledge-Based Systems

Self Cite

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“…Some defects that can occur from the results of printing are crumpled paper, rough paper surface, piling, mottling, imprecise printing, etc. (Ejnarsson, Verikas and Nilsson, 2009;Lundström and Verikas, 2013). Based on factors and defect results in offset printing processes, several companies began to prioritise identifying these two aspects in order to improve the quality of products.…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Quality Prediction in Software-as-A-Service Concept: A Case Study in Offset Printing Company

Arif,

Ramadhan,

Sayf

2023

QIP Journal

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Purpose: This research aims to design a model of quality prediction system using data mining methods in the software-as-a-service (Saas) environment in order to facilitate manufacturers to be able to analyse process and product quality without software investment. Methodology/Approach: To develop the quality prediction model, this study utilized a data mining methodology to extract knowledge from historical data collected from the offset printing industry, focusing on manufacturing parameters. Four classification algorithms (Decision Tree, k-NN, Naive Bayes, Random Tree) were employed and compared to identify the most precise model specifically tailored for offset printing. Subsequently, the prediction model was integrated into a web-based system to enable quality prediction. Findings: This study demonstrates practicality of integrating quality prediction into the SaaS framework, specifically for offset printing. This integration is designed to predict how manufacturing control parameters, such as printing slope angle, engine temperature, paper size, ink density, and roller speed, relate to the occurrence of product defects such as crumpled paper and imprecise printing. Research Limitation/implication: Generalizability is constrained by its focus on the offset printing industry, and prediction accuracy relies on historical data, which can vary across manufacturing sectors, affecting model performance. Originality/Value of paper: This article presents the concept and practical implementation of utilizing data mining in a SaaS environment to enhance the quality of manufacturing, with a particular emphasis on the offset printing sector.

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