Journal of the Korean Wood Science and Technology

The Korean Society of Wood Science & Technology (한국목재공학회)
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Determination of Relationship between Thermal and Mechanical Properties of Wood Material

  • Ozcan, Cemal (Department of Industrial Products Design, Fethi Toker Faculty of Fine Arts and Design. Karabuk University) ;
  • Korkmaz, Mustafa (Department of Wood Products Industrial Engineering, Faculty of Technology, Duzce University)
  • Received : 2018.06.10
  • Accepted : 2019.07.02
  • Published : 2019.07.25
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Abstract

Non-destructive test techniques are becoming increasingly important for assessment and maintenance. These techniques are very useful for assessment of materials such as wood, whose performance can vary considerably depending on the conditions of use. It is possible to estimate some mechanical properties of a material by determining the movement of energy through the material with the help of these techniques. In this study, it was investigated whether the wood material could be tested nondestructively by the heat energy produced by a source. The correlations between the thermal conductivity and mechanical properties of Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea L.) woods were investigated. The thermal conductivity (TC), density, modulus of rupture (MOR), compression strength (CS), and modulus of elasticity (MOE) values of samples were measured according to the related standards and these values were correlated with each other. The linear and multiple regression tests were employed to determine the correlation between thermal conductivity and mechanical properties. The results showed that there is a very strong correlation between thermal conductivity and both density and MOR values. However, the correlations between TC and both MOE and CS were moderate. The results of this study suggest that the thermal conductivity value can be used to estimate the density and some mechanical properties of wood.

Keywords

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Fig. 1. Preparation of samples.

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Fig. 2. The simple linear regression scatter plot of Scots pine samples.

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Fig. 3. The simple linear regression scatter plot of sessile oak samples.

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Fig. 4. The multiple regression matrices of samples.

Table 1. Results of the descriptive statistics of test

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Table 2. Pearson correlations (r) and linear regression (R2)testvalues

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Table 3. Pearson correlations (r) and linear regression (R2) test values

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