Mathematical modelling of specification lumber yield during the sawing of high-quality oak sawlogs
DOI:
https://doi.org/10.36930/42265211Keywords:
pedunculate oak; sawlogs; log sawing; specification lumber; volumetric yield; log diameter; saw kerf width; mathe-matical modelling; regression model; resource efficiencyAbstract
Improving the efficiency of high-quality sawlog utilization is an important objective in sawmilling, particularly when processing valuable hardwood species. The aim of this study was to establish the relationships between oak log diameter, saw kerf width, and the volumetric yield of acceptable specification lumber and to develop a mathematical model for predicting this response. The study investigated the sawing of high-quality pedunculate oak (Quercus robur L.) logs, 4.0 m in length, into lumber with specified dimensions of 4000×200×50 mm. Log diameter d=400–600 mm and saw kerf width s=2.0–5.0 mm were selected as the variable factors. Five replicate determinations were performed for each combination of factors. The experimental data were processed using regression and analysis of variance methods, and a second-order polynomial model was developed. The mean experimental volumetric yield ranged from 49.12 to 61.82 %. Increasing log diameter from 400 to 600 mm increased the predicted yield by approximately 9.30–9.50 %, whereas increasing the saw kerf width from 2.0 to 5.0 mm reduced it by 3.18–3.40 %. Statistically significant linear effects of both factors and a quadratic effect of log diameter were identified. The interaction between the factors and the quadratic effect of saw kerf width were statistically insignificant within the investigated range. The highest predicted yield, approximately 61.83%, was obtained at a log diameter of 600 mm and a saw kerf width of 2.0 mm. The developed mathematical model enables prediction of specification lumber yield and provides a quantitative basis for selecting a rational combination of sawing parameters for the resource-efficient processing of high-quality oak sawlogs.
References
Carreiro G.D. et al. (2021). Sawing Patterns for the Breakdown of Pinus caribaea var. caribaea Wood on Porta-ble Sawmills. Floresta, 51(3), 686–695. https://doi.org/10.5380/rf.v51i3.72332.
Cataldo M.F. et al. (2025). Sawing yield estimation of defective logs in Olea europaea L., Robinia pseudoaca-cia L., and Castanea sativa Mill. species growing in Southern Italy. Annals of Forest Science, 82, 36. https://doi.org/10.1186/s13595-025-01305-7.
Characterization and Yield of Eucalyptus regnans F. Muell Logs for Lumber Production. (2023). Forests, 14, 2359. https://doi.org/10.3390/f14122359.
Cutting force analysis of oak for the development of a cutting force model. (2022). Wood Material Science & Engineering, 17(6), 771–782. https://doi.org/10.1080/17480272.2021.1955296.
Dynamic Generation of Cutting Patterns in Sawmills for Sustainable Planning. (2026). Mathematics, 14(1), 10. https://doi.org/10.3390/math14010010.
Forghani K., Carlsson M., Flener P., Fredriksson M., Pearson J., Yuan D. (2024). Maximizing value yield in wood industry through flexible sawing and product grading based on wane and log shape. Computers and Electron-ics in Agriculture, 216, 108513. https://doi.org/10.1016/j.compag.2023.108513.
Fredriksson M. (2014). Log sawing position optimization using computed tomography scanning. Wood Material Science & Engineering, 9(2), 110–119. https://doi.org/10.1080/17480272.2014.904430.
Genotype–phenotype heuristic approaches for a cutting stock problem with circular patterns. (2013). Engineer-ing Applications of Artificial Intelligence, 26(10), 2349–2355. https://doi.org/10.1016/j.engappai.2013.08.003.
Hinostroza I., Pradenas L., Parada V. (2014). Board cutting from logs: Optimal and heuristic approaches for the problem of packing rectangles in a circle. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2013.04.047.
Improving consistency in hierarchical tactical and operational planning using Robust Optimization. (2020). Computers & Industrial Engineering, 139, 106112. https://doi.org/10.1016/j.cie.2019.106112.
Ištvanić J., Pervan D., Antonović A., Piljak K., Obućina M., Klarić M. (2025). Simulation of the Radial Sawing Technique for Pedunculata Oak (Quercus robur L.) Logs. Forests, 16(10), 1538. https://doi.org/10.3390/f16101538.
Lin W., Wang J. (2012). An integrated 3D log processing optimization system for hardwood sawmills in central Appalachia, USA. Computers and Electronics in Agriculture, 82, 61–74. https://doi.org/10.1016/j.compag.2011.12.014.
Liu C., Jiang Z.H., Zhang S.Y. (2006). Tree-Level Models for Predicting Lumber Volume Recovery of Black Spruce Using Selected Tree Characteristics. Forest Science, 52(6), 694–703. https://doi.org/10.1093/forestscience/52.6.694.
Mayevskyy, V., Ferents, O., Kopynets, Z., Andrashek , Y., & Mayevska, O. (2019). Analysis of volumetric out-put of lumber, taking into account the quality classes of dust raw materials. Forestry, Forest, Paper and Woodwork-ing Industry, 45, 104-110. https://doi.org/10.36930/42194513
Mayevskyy, V., Ferents, O., Marchenko, N., Kopynets, Z., & Andrashek, Y. (2018). On the methodology of re-searching the consumption of dust raw materials for the production of unedged and edged lumber. Forestry, Forest, Paper and Woodworking Industry, 44, 43-50. https://doi.org/10.36930/42184406
Mayevskyy, V., Moroz, R., Kopynets, S., Ferents, O., Shchupakivskyy, R., & Myskiv, Y. (2024). Determining dry oak lumber consumption for the production of solid edge-glued panels. Proceedings of the Forestry Academy of Sciences of Ukraine, (27), 181-188. https://doi.org/10.15421/412425
Moroz, P., Mayevskyy, V., Kopynets, Z., Shchupakivskyy, P., & Myskiv, Y. (2020). Evaluation of the quality of timber sorted according to different standards. Forestry, Forest, Paper and Woodworking Industry, 46, 97-101. https://doi.org/10.36930/42204611
Negeo T.S., Rawat Y.S., Nebiyu M. (2024). The effects of sawing methods on the lumber recovery rate and lum-ber grading of Eucalyptus globulus at the small-scale sawmill enterprise. Journal of the Indian Academy of Wood Science, 21, 345–362. https://doi.org/10.1007/s13196-024-00353-2.
Optimal multiperiod production planning in a sawmill. (2018). Computer Aided Chemical Engineering, 44, 1339–1344. https://doi.org/10.1016/B978-0-444-64241-7.50218-4.
Palma C.D., Vergara F.P. (2016). A Multiobjective Model for the Cutting Pattern Problem with Unclear Prefer-ences. Forest Science, 62(2), 220–226. https://doi.org/10.5849/forsci.14-100.
Parra Galvez J.L.A., Borenstein D., Farias E.S. (2018). Application of optimization for solving a sawing stock problem with a cant sawing pattern. Optimization Letters, 12. https://doi.org/10.1007/s11590-017-1178-x.
Pong W.Y., Cahill J.M. (1988). Lumber recovery from incense-cedar in central California. USDA Forest Service Research Paper PNW-RP-393. https://doi.org/10.2737/PNW-RP-393.
Riesco Muñoz G., Remacha Gete A., Gasalla Regueiro M. (2013). Variation in log quality and prediction of sawing yield in oak wood (Quercus robur). Annals of Forest Science, 70, 695–706. https://doi.org/10.1007/s13595-013-0314-8.
Riesco Muñoz G., Remacha Gete A., Gasalla Regueiro M. (2014). Sawing yield in oak (Quercus robur) wood affected by insect damage. International Biodeterioration & Biodegradation, 86, 102–107. https://doi.org/10.1016/j.ibiod.2013.09.010.
Rinnhofer A., Petutschnigg A., Andreu J.-P. (2003). Internal log scanning for optimizing breakdown. Computers and Electronics in Agriculture, 41, 7–21. https://doi.org/10.1016/S0168-1699(03)00039-5.
Scheduling production for a sawmill: A comparison of a mathematical model versus a heuristic. (2010). Com-puters & Industrial Engineering, 59(4), 667–674. https://doi.org/10.1016/j.cie.2010.07.016.
Simulation-optimisation based framework for Sales and Operations Planning taking into account new products opportunities in a co-production context. (2018). Computers in Industry, 94, 41–51. https://doi.org/10.1016/j.compind.2017.10.002.
Steele P.H. (1984). Factors determining lumber recovery in sawmilling. USDA Forest Products Laboratory General Technical Report FPL-GTR-39. https://doi.org/10.2737/FPL-GTR-39.
Thomas R.E., Buehlmann U. (2022). The Effect of Kerf Thickness on Hardwood Log Recovery. Forest Products Journal, 72(1), 44–51. https://doi.org/10.13073/FPJ-D-21-00065.
Thomas R.E., Buehlmann U. (2023). Effect of Sawing Variation on Hardwood Lumber Recovery—Part I: Vol-ume. Forest Products Journal, 73(1), 59–65. https://doi.org/10.13073/FPJ-D-22-00059.
Thomas R.E., Buehlmann U. (2023). Effect of Sawing Variation on Hardwood Lumber Recovery—Part II: Board Count. Forest Products Journal, 73(1), 66–74. https://doi.org/10.13073/FPJ-D-22-00058.
Thomas R.E., Buehlmann U. (2025). User’s Guide for LORCAT: The Log Recovery Analysis Tool, Version 4.00. USDA Forest Service General Technical Report FPL-GTR-307. https://doi.org/10.2737/FPL-GTR-307.
Thomas R.E., Buehlmann U., Conner D. (2021). LORCAT: A Log Recovery Analysis Tool for Hardwood Sawmill Efficiency. USDA Forest Service Research Paper NRS-33. https://doi.org/10.2737/NRS-RP-33.
Vilkovský P., Klement I., Vilkovská T. (2023). The Impact of the Log-Sawing Patterns on the Quantitative and Qualitative Yield of Beech Timber (Fagus sylvatica L.). Applied Sciences, 13(14), 8262. https://doi.org/10.3390/app13148262.
Zhang S.Y., Liu C. (2006). Predicting the lumber volume recovery of Picea mariana using parametric and non-parametric regression methods. Scandinavian Journal of Forest Research, 21(2), 158–166. https://doi.org/10.1080/02827580500531791.
Zhang S.Y., Tong Q.J. (2005). Modeling lumber recovery in relation to selected tree characteristics in jack pine using sawing simulator Optitek. Annals of Forest Science, 62, 219–228. https://doi.org/10.1051/forest:2005013.



