Effect of Climate and Wood Type on Elastic Modulus of Heat-treated Wood and its Optimization by the Taguchi Method


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Gunes M., Ersin C., ALTUNOK M.

BioResources, vol.19, no.2, pp.3138-3148, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Review
  • Volume: 19 Issue: 2
  • Publication Date: 2024
  • Doi Number: 10.15376/biores.19.2.3138-3148
  • Journal Name: BioResources
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Chemical Abstracts Core, Compendex, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.3138-3148
  • Keywords: Modulus of elasticity in bending, Optimization, Taguchi method, Tannin impregnation, Wooden
  • Gazi University Affiliated: Yes

Abstract

Wood, as the oldest building material, provides some of the basic needs of human beings, including shelter and protection. Wood is used in exterior cladding, carrier systems, joinery, ceiling-floor coverings, windows, doors, and furniture production. When wooden material is exposed to external weather conditions, due to its hygroscopic structure, its physical and mechanical properties deteriorate from exposure to moisture, temperature, and biological organisms. The bending modulus of elasticity of Scots pine (Pinus sylvestris L.), oak (Quercus petraea L.), and chestnut (Castanea sativa M.) wood that was tannin-impregnated and heat-treated at 160 °C, was investigated using Taguchi L9 (33). The sequence was optimized. After heat treatment, the carrier elements were subjected to artificial climate conditions. In the optimization of the data obtained, it was understood that the highest impact factor was the tree type. In contrast, the climate on the elastic modulus was the lowest impact factor. In Taguchi analysis, a mathematical prediction model was created using actual and predicted data using the S/N ratio's biggest-best equation. The R2 of the model can be predicted with an accuracy rate of 98.6%.