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Application of Data Mining Tools in Shrink Sleeve Labels Converting Process

  • Krzysztof KrystosiakEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 183)

Abstract

In manufacturing practices of most big printing companies there are collected data and records of process parameters. Gathering information from this data in form of developed models and rules such as data mining, which uses statistical methods or Artificial Intelligence, Artificial Neural Networks, Decision Trees, Expert Systems, and others are subjects of inter-disciplinary fields of science called Data Mining. In the shrink sleeve production process, the effects of using data mining tools not only improve the quality of the shrink sleeve and winding process but also reduce manufacturing costs. This paper describes how developed models of data mining tools can be used for prediction of initial tension parameters and winding speed for each every new design of shrink sleeve labels. Every one design of shrink sleeve label has a lot of factors. Some of them are more significant, some of them less. The aim of this paper is to choose significant factors and compute a models in learning process using collected data. Finally when models will be computed, can be used for prediction of key winding parameters of each new shrink sleeve label designs. This will bring for company saved time for experimental selection during converting of winding parameters like tension and speed and also will minimize risk of occurrence of defects with incorrect winding parameters.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Production EngineeringWarsaw University of TechnologyWarsawPoland

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