Application of Data Mining Tools in Shrink Sleeve Labels Converting Process

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


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.


  1. 1.
    Demski, T.: Creating and using of Data Mining model with STATISTICA Data Miner Recipe on the example of fraud detection, content of the StatSoft website (2009).
  2. 2.
    Ejsmont, K., Krystosiak, K.: Manufacturing of shrink sleeve labels. In: Economics and Organization of Enterprise, pp. 53–61, 12/2014. Publishing House ORGMASZ, Warsaw (2014)Google Scholar
  3. 3.
    Ejsmont, K., Krystosiak, K., Lipiak, J.: Application of selected data mining technique in printing industry. In: Conference: Polish Association for Production Management, vol. II, pp. 75–86. Publishing House of The Polish Society of Production Management, Opole (2015)Google Scholar
  4. 4.
    Flexography: Principles and Practices, 5th edn. Foundation of Flexographic Technical Association, Inc. (1999)Google Scholar
  5. 5.
    Forum for Shrink Sleeve Technology Development in Poland.
  6. 6.
    Friedmann, J.H.: Multivariate adaptive regression splines. Ann. Stat. 1–67 (1991)Google Scholar
  7. 7.
    Heat Shrink Sleeve Label Technical Manual & Test Methods. Technical Publication, AWA Alexander Watson Associates, Amsterdam (2014)Google Scholar
  8. 8.
    Kipphan, H.: Handbook of Print Media. Technologies and Production Methods. Springer, Germany (2001)Google Scholar
  9. 9.
    Kit, L.Y.: The Wiley Encyclopedia of Packaging Technology, 3rd edn. Wiley (2009)Google Scholar
  10. 10.
    Knosala, R.: Applications of Artificial Intelligence in Production Engineering. WNT, Warsaw (2002)Google Scholar
  11. 11.
    Krystosiak, K., Werpachowski, W.: The improvements of the quality level of packaging with overprint. In: Economics and Organization of Enterprise, pp. 55–64, nr 11/2013. Publishing House ORGMASZ, Warsaw (2013)Google Scholar
  12. 12.
    Krystosiak, K., Werpachowski, W.: Advanced data mining methods as a key to improvement of shrink sleeve labels production process. In: Conference: Product & Packaging—Contemporary Challenges 2014, Lodz University of Technology (2014)Google Scholar
  13. 13.
    Krystosiak, K., Werpachowski, W.: Control method of winding quality in shrink sleeve labels converting process. In: Computer Methods in Material Science, vol. 15, 3/2015. Publishing House AGH University of Science and Technology, Krakow (2015)Google Scholar
  14. 14.
    Krystosiak, K.: Impact study of artificial neural network topology on prediction quality of winding parameters. In: Opakowanie, pp. 60–64, 2/2016. Publishing House SIGMA-NOT, Warsaw (2016)Google Scholar
  15. 15.
    Krystosiak, K.: Prediction method for winding parameters in label converting process with data mining tools. In: 7th International Conference on Engineering, Project, and Production Management, Bialystok 2016, Procedia Engineering. Elsevier (2016)Google Scholar
  16. 16.
    Nisbet, R., Elder, J., Miner, G.: Handbook of Statistical Analysis & Data Mining Applications. Academic Press/Elsevier (2009). ISBN: 978-0-12-374765-5Google Scholar
  17. 17.
    Roisum D.R.: How to measure roll quality. Tappi J. 10/1988 (1988)Google Scholar
  18. 18.
    StatSoft Electronic Statistics Textbook, StatSoft.
  19. 19.
    Tadeusiewicz, R.: Neural Networks. Academic Publishing House, Warsaw (1993)Google Scholar
  20. 20.
    Walker, T.J.: Stress & strain. In: Paper, Film & Foil Converter, nr 6/2009. Penton Media Publication (2009)Google Scholar
  21. 21.
    Walker, T.J.: What is the right tension? In: Paper, Film & Foil Converter, nr 12/2009. Penton Media Publication (2009)Google Scholar
  22. 22.
    Web Handling website.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Production EngineeringWarsaw University of TechnologyWarsawPoland

Personalised recommendations