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European Journal of Wood and Wood Products

, Volume 77, Issue 6, pp 1211–1220 | Cite as

Robust optimization of energy consumption during mechanical processing of wood

  • Diego Jean de MeloEmail author
  • Taiane Oliveira Guedes
  • José Reinaldo Moreira da Silva
  • Anderson Paulo de Paiva
Original
  • 42 Downloads

Abstract

Aiming for mechanical processing with efficiency and performance, robust optimization techniques are important tools. The wood processing encourages these studies, since wood has a heterogeneous behavior during cutting and reacts in a hardly predictable way to process variations. The objective of this work was to find the equilibrium point between the mean and the variance of the cutting energy consumption, thus indicating optimal processing parameters that do not underlie the influence of the moisture variable. For this, 30 boards of Pinus taeda were processed in a planer with a frequency inverter, which allowed to capture the processing data. The control variables for this process were: cutting motor rotation, feed motor rotation and radial depth of cut. With the voltage and current data, the specific energy was calculated. The optimum point for lower power consumption was determined by the mean square error (MSE) where the cutting motor rotation, feed motor rotation and cutting depth values were 1831.38 rpm, 835.22 rpm and 1.16 mm, respectively. Thus, the effect of moisture was neutralized.

Notes

Acknowledgements

The authors gratefully acknowledge CNPq, CAPES, FAPEMIG, IEPG/UNIFEI and DCF/UFLA for supporting this research.

References

  1. ABNT (2003) NBR 11941: Wood: determination of basic density. Brazilian Association of Technical Standards, Rio de Janeiro, p 6pGoogle Scholar
  2. Aguilera A, Martin P (2001) Machining qualification of solid wood of Fagus sylvatica L. and Picea excelsa L.: cutting forces, power requirements and surface roughness. Eur J Wood Prod 59:483–488CrossRefGoogle Scholar
  3. Al-Ghamdi KA (2013) Robust parameter design of an EDM process. Qual Reliab Eng Int 29:921–934CrossRefGoogle Scholar
  4. Anderson-Cook CM, Borror CM, Montgomery DC (2009) Response surface design evaluation and comparison. J Stat Plan Inference 139:629–641CrossRefGoogle Scholar
  5. Barcík Š, Kminiak R, Řehák T, Kvietková M (2010) The influence of selected factors on energy requirements for plain milling of beech wood. J For Sci 56:243–250CrossRefGoogle Scholar
  6. Bendikiene R, Keturakis G (2017) The influence of technical characteristics of wood milling tools on its wear performance. J Wood Sci 63:606–614CrossRefGoogle Scholar
  7. Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J Roy Stat Soc 13:1–45Google Scholar
  8. Costa DMD, Brito TG, Paiva AP, Leme RC, Balestrassi PP (2016) A normal boundary intersection with multivariate mean square error approach for dry end milling process optimization of the AISI 1045 steel. J Clean Prod 135:1658–1672CrossRefGoogle Scholar
  9. Dehnad K (1989) Quality control, robust design, and the Taguchi method. Springer, BostonCrossRefGoogle Scholar
  10. Evangelaras H, Koukouvinos C (2006) Robust parameter design: optimization of combined array approach with orthogonal arrays. J of Stat Plan Inference 136:3698–3709CrossRefGoogle Scholar
  11. Gremyr I, Silva V, Raharjo H, Goh TN (2014) Adapting the robust design methodology to support sustainable product development. J Clean Prod 79:231–238CrossRefGoogle Scholar
  12. Hill WJ, Hunter WG (1966) A review of response surface methodology: a literature survey. Am Soc Qual 8:571–590Google Scholar
  13. Kisser J, Ylinen A, Freudenberg K, Kollmann F, Liese W, Thunell B, Winkelmann H, Côté W, Koch P, Marian J, Stamm A (1967) History of wood science. Wood Sci Technol 1:161–190Google Scholar
  14. Kollmann F, Coté WA (1968) Principles of wood science and technology I: solid wood. Springer, BerlinCrossRefGoogle Scholar
  15. Kretschmann DE, Green DW (2007) Modeling moisture content-mechanical property relationships for clear southern pine. Wood Fiber Sci 28:320–337Google Scholar
  16. Kuhn AM (2003) Optimizing response surface experiments with noise factors using confidence regions. Qual Eng 15:419–426CrossRefGoogle Scholar
  17. Lin DKJ, Tu W (1995) Dual response surface optimization. J Qual Technol 27:34–39CrossRefGoogle Scholar
  18. Matsuura S, Suzuki H, Lida T, Kure H, Mori H (2011) Robust parameter design using a supersaturated design for a response surface model. Qual Reliab Eng Int 27:541–554CrossRefGoogle Scholar
  19. Montgomery DC (2017) Design and analysis of experiments. Wiley, ArizonaGoogle Scholar
  20. Myers RH, Montgomery DC, Vining GG, Borror CM, Kowalski SM (2004) Response surface methodology: a retrospective and literature survey. J Qual Technol 36:53–77CrossRefGoogle Scholar
  21. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: Process and product optimization using designed experiments. Wiley series in probability and statistics, New JerseyGoogle Scholar
  22. Nair VN, Abraham B, Mackay J, Box G, Kacker RN, Lorenzen TJ, Lucas JM, Myers RH, Vining GG, Nelder JA, Phadke MS, Sacks J, Welch WJ, Shoemaker AC, Tsui KL, Taguchi S, Wu CFJ (1992) Taguchi’s parameter design: a panel discussion. Technometrics 34:127–161CrossRefGoogle Scholar
  23. Naves FL, de Paula TI, Balestrassi PP, Braga WLM, Sawhney RS, Paiva AP (2017) Multivariate Normal Boundary Intersection based on rotated factor scores: a multiobjective optimization method for methyl orange treatment. J Clean Prod 143:413–439CrossRefGoogle Scholar
  24. Niemz P (2003) Physik des Holzes und der Holzwerkstoffe (Physics of wood and wood-based products). DRW-Verlag, Leinfelden-EchterdingenGoogle Scholar
  25. Paiva AP, Paiva EJ, Ferreira JR, Balestrassi PP, Costa SC (2009) A multivariate mean square error optimization of AISI 52100 hardened steel turning. Int J Adv Manuf Technol 43:631–643CrossRefGoogle Scholar
  26. Paiva AP, Gomes JHF, Peruchi RS, Leme RC, Balestrassi PP (2014) A multivariate robust parameter optimization approach based on principal component analysis with combined arrays. Comput Ind Eng 74:186–198CrossRefGoogle Scholar
  27. Phadke MS (1989) Quality engineering using robust design. Prentice Hall, Nova JerseyGoogle Scholar
  28. Skaar C (1988) Wood–water relations. Springer, BerlinCrossRefGoogle Scholar
  29. Sofuoglu SD (2017) Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (Pinus sylvestris L.) using Taguchi design method. Eur J Wood Prod 75:33–42CrossRefGoogle Scholar
  30. Souza EMD, Silva JRMD, Lima JT, Napoli A, Raad TJ, Gontijo TG (2011) Specific cutting energy consumption in a circular saw for Eucalyptus stands VM01 and MN463. Cerne 17(1):109–115CrossRefGoogle Scholar
  31. Sutcu A (2013) Investigation of parameters affecting surface roughness in CNC routing operation on wooden EPG. BioResources 8:795–805CrossRefGoogle Scholar
  32. Tan MHY (2009) Estimation of the mean and variance response surfaces when the means and variances of the noise variables are unknown. IIE Trans 41:942–956CrossRefGoogle Scholar
  33. Thibaut B, Denaud L, Collet R, Marchal R, Beauchêne J, Mothe F, Méausoone P, Martin P, Larricq P, Eyma F (2016) Wood machining with a focus on French research in the last 50 years. Ann For Sci 73:163–184CrossRefGoogle Scholar
  34. Tiryaki S, Hamzaçebi C, Malkoçoglu A (2015) Evaluation of process parameters for lower surface roughness in wood machining by using Taguchi design methodology. Eur J Wood Prod 73:537–545CrossRefGoogle Scholar
  35. Ugulino B, Hernández RE (2017) Assessment of surface properties and solvent-borne coating performance of red oak wood produced by peripheral planning. Eur J Wood Prod 75:581–593CrossRefGoogle Scholar
  36. Vining GG, Myers RH (1990) Combining Taguchi and response surface philosophies: a dual response approach. J Qual Technol 22:38–45CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Industrial Engineering and ManagementFederal University of ItajubáItajubáBrazil
  2. 2.Department of Forestry SciencesFederal University of LavrasLavrasBrazil

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