Study of Thickness Variability of the Floorboard Surface Layer

  • Agnieszka KujawińskaEmail author
  • Michał Rogalewicz
  • Magdalena Diering
  • Krzysztof Żywicki
  • Adam Hamrol
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 183)


A characteristic feature of the wood machining processes is a high level of data uncertainty from these processes. This uncertainty is due to the influence of material environment, but also of the measurement system. On the one hand, the quality of the data is affected by the inhomogeneity of raw wood material, the uniqueness of its structure, the randomness of its natural defects, wood deformation and because of nature of wood, which depends on the conditions/environmental parameters. Moreover, the uncertainty of the measured data results from the engineering problems associated with the inaccuracy of cutting process and method of measuring the geometrical characteristics of wood. The subject of authors research is production process of the floorboard surface layer (lamella). Due to the material loss in the process of equalization of cut off wood plies, there is a need to redesign (develop) methodology of the rules to select material allowances. One of the stages of research leading to that is to describe statistical models which explain the nature of the observed variability of chosen woodworking operations. One of the critical features in the production process of the floorboard surface layer is its thickness. The paper discusses the engineering problems associated with the measuring method of this feature. Based on the observations and research results, a new methodology for measuring the thickness of the lamella is proposed. The study proposes a method and organization of measurement performance—the number and layout (net) of measuring points (grid points) on the surface of the lamella, the choice of measuring instruments, and the size and frequency of sampling. The proposed approach allowed to increase the usefulness of measurement results. According to the new methodology, date were acquired. Based on this—for chosen woodworking operations: cutting, drying and grinding—the statistical models of lamella thickness variation were built. The article describes these three models. These models will be used to decide whether to redesign a model of tolerance and the methodology of the rules to select material allowances for each operation. The authors formulate conclusions and recommendations to improve the methodology of the lamella thickness measuring.


Raw wood Floorboard surface layer (lamella) Thickness measure Statistical models 



The presented results are derived from a research project “Improvement of raw wood efficiency in the industrial production processes” (EFFraWOOD), conducted by Chair of Management and Production Engineering, Faculty of Mechanical Engineering and Management, Poznan University of Technology, Poland, supported by the National Centre for Research and Development (NCBR) within the framework of the strategic R&D program “Environment, agriculture and forestry”—BIOSTRATEG.


  1. 1.
    Grabowska, M., Takala, J.: Assessment of quality management system maturity. In: Hamrol, A., Ciszak, O., Legutko, S., Jurczyk, M. (eds.) Advances in Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Cham (2018)Google Scholar
  2. 2.
    Perzyk, M.: Zastosowanie modelowania miękkiego do wykrywania przyczyn zakłóceń procesów odlewniczych. Możliwości i problemy, Materiały na XXXI Sympozjum Naukowo – Techniczne Zakładu Odlewnictwa ITMat, Warszawa (2006)Google Scholar
  3. 3.
    Putnik, G.D., Škulj, G., Vrabič, R., Varela, L., Butala, P.: Simulation study of large production network robustness in uncertain environment. CIRP Ann. Manuf. Technol. 64(1), 439–442 (2015).
  4. 4.
    Więcek-Janka, E., Mierzwiak, R., Kijewska, J.: Competencies’ model in the succession process of family firms with the use of grey clustering analysis. J. Grey Syst. 28(2), 121–131 (2016)Google Scholar
  5. 5.
    Trojanowska, J., Kolinski, A., Galusik, D., Varela, M.L.R., Machado, J.: A methodology of improvement of manufacturing productivity through increasing operational efficiency of the production process, In: Hamrol, A., Ciszak, O., Legutko, S., Jurczyk, M. (eds.) Advances in Manufacturing. Lecture Notes in Mechanical Engineering, pp. 23–32. Springer, Cham (2018)Google Scholar
  6. 6.
    Starzyńska, B., Szajkowska, K., Diering, M., Rocha, A., Reis L.P.: A study of raters agreement in quality inspection with the participation of hearing disabled employees. In: Advances in Manufacturing. Lecture Notes in Mechanical Engineering, pp. 881–888. Springer (2018).
  7. 7.
    Patalas-Maliszewska, J., Kłos, S.: A study on improving the effectiveness of a manufacturing company in the context of knowledge management—research results. Found. Manag. Int. J. 9(1), 149–160 (2017). ISSN: 2080-7279.
  8. 8.
    Kujawińska, A., Diering, M., Rogalewicz, M., Żywicki, K., Hetman, Ł.: Soft modelling-based methodology of raw material waste estimation. In: Burduk, A., Mazurkiewicz, D. (eds.) Intelligent Systems in Production Engineering and Maintenance—ISPEM 2017. Advances in Intelligent Systems and Computing, vol. 637, Springer, Cham (2018)Google Scholar
  9. 9.
    Główny Urząd Statystyczny, Przemysł i budownictwo (2015)Google Scholar
  10. 10.
    Sandvik, Production, use and maintenance of wood bandsaw blades. A manual from Sandvik Steel. AB Sandvik Steel, Sandviken, Sweden, May 1999, p. 336Google Scholar
  11. 11.
    Orłowski, K.A., Walichnowski, A.: Economic analysis of upper layer production of engineered floorings. Wood Res. Pap. Rep. Announc. (Drew.) 56(189), 115–126 (2013)Google Scholar
  12. 12.
    Report of the project: Improvement of raw wood efficiency in the industrial production processes, EFFraWOOD, BIOSTRATEG2/298950/1/NCBR/2016, the Poznan University of Technology, Poland (in cooperation with BARLINEK Inwestycje Ltd., a floorboard manufacturer in Poland), supported by the National Centre for Research and Development from the financial means within the BIOSTRATEG programme (2015–2018) (2017)Google Scholar
  13. 13.
    Sydor, M.: Drewno w budowie maszyn, Historia najważniejszego tworzywa, Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu, Poznań (2011)Google Scholar
  14. 14.
    PN-EN 13810-1:2004, Płyty drewnopochodne – Posadzki pływające – Część 1: Wymagania użytkowe i techniczne (2004)Google Scholar
  15. 15.
    PN-D-02006:2000, Surowiec drzewny – Odbiorcza kontrola jakości według metody alternatywnej – Terminy, definicje, metody badań (2000)Google Scholar
  16. 16.
    Kujawińska, A., Rogalewicz, M., Diering, M., Hamrol, A.: Statistical approach to making decisions in manufacturing process of floorboard. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds.) Recent Advances in Information Systems and Technologies. WorldCIST, Advances in Intelligent Systems and Computing, vol. 571, pp. 499–508. Springer (2017)Google Scholar
  17. 17.
    AIAG-Work Group, Measurement System Analysis, 4th edn., Reference manual, AIAG-Work Group, Daimler Chrysler Corporation, Ford Motor Company, General Motors Corporation (2010)Google Scholar
  18. 18.
    Perez-Wilson, M.: Gauge R&R studies for destructive and non-destructive testing (2007)Google Scholar
  19. 19.
  20. 20.
    Non-parametric Games-Howell test background. Accessed Sept 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Agnieszka Kujawińska
    • 1
    Email author
  • Michał Rogalewicz
    • 1
  • Magdalena Diering
    • 1
  • Krzysztof Żywicki
    • 1
  • Adam Hamrol
    • 1
  1. 1.Faculty of Mechanical Engineering and ManagementPoznan University of TechnologyPoznańPoland

Personalised recommendations