93
Views
2
CrossRef citations to date
0
Altmetric
Original Article

A comprehensive assessment on surface quality of machined wooden products via Box-Behnken design method

, &
Pages 896-905 | Received 26 Aug 2023, Accepted 28 Nov 2023, Published online: 17 Dec 2023

References

  • Ayanleye, S., et al., 2021. Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression. European Journal of Wood and Wood Products, 79, 101–115. doi:10.1007/s00107-020-01621-x.
  • Borkowski, J.J., 1995. Spherical prediction-variance properties of central composite and Box—Behnken designs. Technometrics, 37 (4), 399–410. doi:10.1080/00401706.1995.10484373.
  • Bezerra, M.A., et al., 2008. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76 (5), 965–977. doi:10.1016/j.talanta.2008.05.019.
  • Box, G.E., and Draper, N.R., 1987. Empirical model-building and response surfaces. Washington: John Wiley & Sons.
  • Cakmak, A., et al., 2023. Optimization of wood machining parameters using artificial neural network in CNC router. Materials Science and Technology, 39 (14), 1728–1744. doi:10.1080/02670836.2023.2180901.
  • Csanády, E., and Magoss, E., 2013. Mechanics of wood machining. Springer. doi:10.1007/978-3-030-51481-5
  • Csanády, E., Magoss, E., and Tolvaj, L., 2015. Quality of machined wood surfaces. Springer. doi:10.1007/978-3-319-22419-0
  • Davim, J.P., 2013. Wood machining. Portugal: John Wiley & Sons.
  • Demir, A., et al., 2022. Determination of CNC processing parameters for the best wood surface quality via artificial neural network. Wood Material Science & Engineering, 17 (6), 685–692. doi:10.1080/17480272.2021.1929466.
  • Eriksson, L., et al., 2000. Design of experiments, principles and applications, learn ways AB: Stockholm. Umetrics UK Ltd.
  • Ghasemi, A., and Vanini, S.A.S., 2023. A comprehensive investigation on the effect of controlling parameters of ultrasonic peening treatment on residual stress and surface roughness: Experiments, numerical simulations and optimization. Surface and Coatings Technology, 464, 129515. doi:10.1016/j.surfcoat.2023.129515.
  • Guo, X., et al., 2021. Cutting forces and cutting quality in the up-milling of solid wood using ceramic cutting tools. The International Journal of Advanced Manufacturing Technology, 114, 1575–1584. doi:10.1007/s00170-021-06991-x.
  • Iskra, P., and Hernández, R.E., 2012. Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction. Wood Science and Technology, 46, 115–128. doi:10.1007/s00226-010-0378-7.
  • Kilic, M., et al., 2006. Effect of machining on surface roughness of wood. Building and Environment, 41 (8), 1074–1078. doi:10.1016/j.buildenv.2005.05.008.
  • Koc, K.H., et al., 2017. Effect of CNC application parameters on wooden surface quality. Measurement, 107, 12–18. doi:10.1016/j.measurement.2017.05.001.
  • Koch, P., 1964. Wood machining processes: CAB direct. New York: Ronald Press Co.
  • Li, J., et al., 2022. Optimization of squeeze casting process of gearbox cover based on FEM and Box-Behnken design. The International Journal of Advanced Manufacturing Technology, 118, 3421–3430. doi:10.1007/s00170-021-08099-8.
  • Pelit, H., et al., 2021. Surface roughness of thermally treated wood cut with different parameters in CNC router machine. BioResources, 16 (3), 5133–5147. doi:10.15376/biores.16.3.5133-5147.
  • Rabiei, F., and Yaghoubi, S., 2023. A comprehensive investigation on the influences of optimal CNC wood machining variables on surface quality and process time using GMDH neural network and bees optimization algorithm. Materials Today Communications, 36, 106482. doi:10.1016/j.mtcomm.2023.106482.
  • Razaei, F., et al., 2020. Surface quality measurement by contact and laser methods on thermally modified spruce wood after plain milling. The International Journal of Advanced Manufacturing Technology, 110, 1653–1663. doi:10.1007/s00170-020-05983-7.
  • Shukla, A., et al., 2017. Applications of TOPSIS algorithm on various manufacturing processes: A review. Materials Today: Proceedings, 4 (4), 5320–5329. doi:10.1016/j.matpr.2017.05.042.
  • Singer, H., and Özşahin, Ş, 2022. Prioritization of factors affecting surface roughness of wood and wood-based materials in CNC machining: A fuzzy analytic hierarchy process model. Wood Material Science & Engineering, 17 (2), 63–71. doi:10.1080/17480272.2020.1778079.
  • Sofuoglu, S.D., 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. European Journal of Wood and Wood Products, 75 (1), 33–42. doi:10.1007/s00107-016-1028-z.
  • Tiryaki, S., et al., 2014. Using artificial neural networks for modeling surface roughness of wood in machining process. Construction and Building Materials, 66, 329–335. doi:10.1016/j.conbuildmat.2014.05.098.
  • Torkghashghaei, M., et al., 2023. Effect of variable engineered micro-geometry of the cutting edges of circular saws on the surface quality of SPF boards. European Journal of Wood and Wood Products, 81, 1261–1276. doi:10.1007/s00107-023-01961-4.
  • Wu, X., et al., 2022. Research on GA-BP neural network model of surface roughness in air drum sanding process for poplar. European Journal of Wood and Wood Products, 80, 477–487. doi:10.1007/s00107-021-01686-2.
  • Xiao, G., et al., 2023. An intelligent parameters optimization method of titanium alloy belt grinding considering machining efficiency and surface quality. The International Journal of Advanced Manufacturing Technology, 125 (1-2), 513–527. doi:10.1007/s00170-022-10723-0.
  • Zhu, Z., et al., 2022. Assessment of surface roughness in milling of beech using a response surface methodology and an adaptive network-based fuzzy inference system. Machines, 10 (7), 1–12. doi:10.3390/machines10070567.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.