Optimization of the manufacturing of Metasequoia-based three-layer structure parquet flooring by a response surface methodology

On the basis of a single-factor experiment, a mathematical model was established by the response surface analysis method based on the Box-Behnken experimental design principle. The effects of three factors, including hot-pressing temperature, hot-pressing time, and hot-pressing pressure, and their interactions on the modulus of rupture (MOR) of Metasequoia-based three-layered structure parquet flooring were studied. The results show that the quadratic polynomial model in the regression equation is significant, and the correlation between the value predicted by the model and the experimental value is 91.17%. The optimized best hot-pressing process parameters are determined to be as follows: hot-pressing temperature of 96.03°C, hot-pressing time of 6.70 min, and hot-pressing pressure of 8 kg·cm-2. Under these conditions, the best MOR are obtained, reaching a value of 102.05 MPa. The theoretically predicted value is in good agreement with the experimental results.

The potential of producing high added value structural timber from lamellae waste. Test results and analysis

The research was based on the analysis of the density, bending strength and modulus of elasticity of 100 oak lamellae generated as small-sized production waste. In this part of the study series, the test results were presented in detail and analysed, in particularly the density distribution. Correlations between some test results have been shown. The dynamic and static test results were also compared. Despite the poor quality lamellae, the average density of the sample set corresponds to literary values and the distribution of density is normal. Specimens with low density are unsuitable for further use. But the density alone cannot be used for classification. Between static and dynamic modulus of elasticity can be found a good relationship. The relationships between density and both static and dynamic modulus of elasticity of the specimens can be considered as good, too. The best correlation is in bending tests between the deflection of the specimens in the elastic range and the bending strength.

Short notes: Can the physical properties of wood samples be predicted from photographs displayed on a monitor?

This study focused on evaluating the physical properties of wood from photographs displayed on a monitor. Sample photos of 475 hardwoods and their physical information were collected from a wood database. R, G, and B values were extracted from the wood photos using color picker software. Statistical techniques such as Pearson’s correlation coefficient and multiple regression analysis were applied to investigate relationships between wood color and physical properties. From results of Pearson’s correlation coefficient, R, G, and B values were most affected by specific gravity. In a multiple regression analysis, tree size, specific gravity, and modulus of rupture (MOR) were significant in the positive (+) direction by color (R, G, and B). On the other hand, modulus of elasticity (MOE) was significant in the negative (-) direction at the 1% level by color. The specific gravity of wood had the most significant effect on R, G, and B values in multiple regression analysis. In conclusion, the color and specific gravity of wood were related closely. Additionally, it is possible to predict the physical properties of wood from the R, G, and B values of a wood sample photograph displayed on a monitor. These results could provide useful information for wood researchers as well as wood exporters and importers.