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Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood
Journal Pre-proofs Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood Zongying Fu, Fan Zhou, Xin Gao, Xiang Weng, Yongdong Zhou PII: DOI: Reference:
S0263-2241(19)31081-4 https://doi.org/10.1016/j.measurement.2019.107215 MEASUR 107215
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Measurement
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22 July 2019 25 September 2019 25 October 2019
Please cite this article as: Z. Fu, F. Zhou, X. Gao, X. Weng, Y. Zhou, Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood, Measurement (2019), doi: https://doi.org/10.1016/ j.measurement.2019.107215
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Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood Zongying Fu, Fan Zhou, Xin Gao, Xiang Weng, Yongdong Zhou* Research Institute of Wood Industry, Chinese Academy of Forestry; Key Lab of Wood Science and Technology of Status Forestry Administration, Beijing 150040, P.R. China
Abstract In this study, the modulus of elasticity (MOE) and modulus of rupture (MOR) of heat treatment (HT) radiata pine wood were assessed by artificial neural network (ANN) models based on the changes of chromatic values. In the established ANN models, the chromatic values, i.e. L*, a* and b* conjunction with HT temperature were as input variables, the MOE and MOR as output parameters. According to the results, the values of L*, a* and b* had a good consistency with HT intensity. The MOE changed with HT temperature in a fluctuating tendency, whereas the MOR generally deteriorated with increasing HT temperature. As for the established ANN models, the predicted values were in good agreement with the measured ones, the determination coefficient of the prediction model for MOE and MOR were 0.904 and 0.783, respectively.
Keywords Artificial neural networks; chromatic values; heat treatment wood; modulus of elasticity; modulus of rupture
Corresponding author: Yongdong Zhou, Research Institute of Wood Industry, Chinese Academy of Forestry; Key Lab of Wood Science and Technology of Status Forestry Administration, Beijing, 100091, P.R. China; E-mail: [email protected]
1 Introduction As an environmentally friendly modification method, heat treatment (HT) has been widely used in wood industry. The underlying principle of HT is the thermal degradation and molecular reconstruction of wood cell wall matter, especially for the hemicelluloses with low molecular weight. It is reported that HT had some superiority in improving wood properties, such as decreasing wood hygroscopicity, improving wood dimensional stability as well as increasing biological durability [1-4]. However, when wood is exposed to a high temperature for a long duration, it becomes more brittle and the certain mechanical properties of wood will be lower because of the thermal degradation and weight losses [5-7]. 1
In addition, the color changes are also one of the important features for HT wood, which are closely related to HT temperature and duration. Several studies found that there are strong correlations between total color difference and mechanical properties in HT wood. Thus, the color parameters can be estimated quantitatively and used as a prediction of wood strength [8-10]. Many studies have been carried out on the changes of mechanical properties in HT wood. Shi et al. [11] studied the mechanical properties of spruce, pine, fir, aspen and birch heat-treated at temperatures of 200 °C or higher. As a result, the MOR decreased by 0-49% for spruce, pine, fir and aspen but for birch the MOR increased slightly. And the MOE of spruce and pine decreased between 4-28 %, however, for fir, aspen and birch the MOE increased. The samples of beech wood were heat-treated at 150, 175, and 200 °C for 1, 3 and 5 h by Percin et al. [12], which found that a small increase was determined in the MOR, MOE and Brinell hardness at 150 °C for 1 and 3 h. However, all of them were clearly decreased at higher temperature and duration. Kocaefe et al. [13] reported that the mechanical properties (MOE, MOR, hardness, etc.) of HT jack pine wood generally deteriorated at HT temperatures higher than 200 °C compared to those of untreated wood. From the results of above literatures, the effects of HT on mechanical properties were various at HT temperatures below than 200 °C, but for HT temperatures above than 200 °C, most of the mechanical properties in HT wood clearly decreased, and there was some difference in reduction level for different wood species, HT temperature and duration. To obtain the changes of mechanical properties for different HT process, a large number of comprehensive experiments with procedural difficulties and long times need to be carried out, thus it is necessary to explore suitable modeling methods for the investigation of mechanical properties in HT wood. As a fascinating mathematical tool without assumptions and knowing a priori the model structure, artificial neural networks (ANNs) have been widely used in the field of wood evaluation and processing. The thermal conductivity [14], dielectric loss factor [15], density [16], equilibrium moisture content [17] of wood have been predicted by different ANN models. Watanabe et al. [18] evaluated the final moisture content of sugi wood samples after air-drying by an ANN model according to initial moisture content, basic density, heartwood ratio, lightness, annual ring orientation and width. Fu et al. [19, 20] developed three layers feedforward perception network models to predict elastic strain and mechanosorptive creep of white birch disks during conventional drying. In addition, there are also a few studies on predicting MOE and MOR by ANN models. The prediction of MOE and MOR of structural plywood board by ANN was reported by Fernández et al. [21], which demonstrated the high capacity of ANN for modelling complex relations between variables. The wood species, HT temperature and time were considered as the input variables to predict MOE and MOR of HT woods have been reported by Tiryaki et al., who also developed an ANN model to predict compression strength of HT wood parallel to grain, indicated that the ANN model provided high prediction precision compared with multiple linear regression 2
model [22, 23]. In this study, an ANN model was established to predict MOE and MOR in HT radiata pine wood, considering chromatic values, i.e. lightness (L*), red/green (a*), blue/yellow (b*) conjunction with HT temperature as input variables. The main objective of this research is to predict MOE and MOR values of HT wood with different HT temperature and duration in a non-destructive, swift and inexpensive way.
2 Materials and Methods 2.1 Materials Radiata pine (Pinups radiata D. Don) lumbers were imported from New Zealand with the diameter above 50 cm at breast height and the basic density of 0.45 g/cm3. The lumbers were sawed into boards of 25 mm in thickness and dried to average moisture content about 12% in Shandong Lichen Group Co., LTD. (Shandong province, China). The boards without visible defects were picked out and sawed into HT specimens with the dimensions of 500 mm × 120 mm × 25 mm (Longitudinal × Tangential × Radial, L × T × R) for HT experiment and color measurement. Afterwards, the wood specimens were cut into the test samples for MOE and MOR measurement with dimensions of 300 mm × 20 mm × 20 mm (L × T × R) according to GB/T 1929-2009 [24]. 2.2 HT experiments The HT experiments were conducted in a self-designed laboratory HT equipment at atmospheric pressure, in which superheated steam as shielding gas. Four different temperatures (185, 200, 215 and 230 °C) and a certain duration of 4 h were applied to wood specimens. Thus, wood specimens were randomly divided into four HT groups of having twenty specimens in each, and the nontreated wood specimens as the control of twenty specimens were also considered for the following tests. The HT process included pre-heating period, drying period, temperature rise period, HT period and temperature fall period (Table 1). For each period, the oxygen concentration was monitored by a transducer and controlled by changing the water vapor content. Table 1 The process design phases for HT experiment Process phase
Temperature (°C)
Oxygen concentration (%)
Time (h)
Pre-heating Drying Temperature rise HT Temperature fall
100 130 180 185/200/215/230 100
16 13 5 2 16
1 2 1 4 1
2.3 Surface color measurements of HT wood 3
The three-dimensional CIE L*a*b* color space system was employed in this test [25, 26]. Six marked points on the surface of each specimen were measured to obtain an objective characterization of the specimen color by a tristimulus color meter CR-400 (Konica Minolta (China) Investment LTD.). The values of vertical coordinate L*, the chromatic coordinates a* and b*were recorded. After measuring the color coordinates, the total color change ∆E and C* was calculated using Eq. (1) and (2), respectively.
E (L* )2 (a * )2 (b * )2 (1)
C * (a * )2 (b * )2 (2) where ∆E is the total color difference, ∆L* is the difference in lightness between the control and HT groups, ∆a* and ∆b* are the difference in chromatic coordinates between the control and HT groups, and C* is the chromatic saturation. 2.4 Mechanical properties test of HT wood The MOE and MOR were determined by universal mechanical testing machine. In each HT temperature level, thirty standard samples for mechanical properties with dimension of 300 mm × 20 mm × 20 mm (L × T × R) were used to the test. The loading was done at the tangential directions of samples. Prior to mechanical properties test, the moisture content of test samples was balanced to 12% in a conditioning chamber. The MOE was measured firstly according to GB/T 1936.2-2009 [27], and thereafter MOR was measured based on GB/T 1936.1-2009 [28]. After determining MOE and MOR, the moisture content of the test samples was measured, and transformed the mechanical strength value to 12% moisture content using Eq. (3).
12 m 1 M 12
(3)
where δ12 is the strength value at moisture content of 12%, δm is the strength value at the moisture content of M, is the constant value showing relationship between strength and moisture content, M is the moisture content of the test samples during the mechanical properties test. 2.5 ANN model Inspired by the functional behavior of the biological nervous system, ANN modeling is particularly useful for dealing with the nonlinearities and complexities of ill-defined processes using past historical data, even if all mechanisms and principles are not clarified; further, the network can be built directly from experimental data by the self-organizing capabilities without any prior assumptions [29, 30]. In general, the basic information processing elements of the neural network operation are called neurons. Some of the neurons interface with the outside world to receive information called input layer, and other neurons communicate the prediction to the outside world called output layer. All the rest of the neurons connect the input layer to the output layer called hidden 4
layers. The network function is determined largely by the interconnections between neurons, which are not simple connections but some non-linear functions [19]. The Back-Propagation neural networks were used in this study. As shown in Figure 1, the HT temperature, chromatic values including L*, a* and b* were considered as input variables, the MOE and MOR were used as outputs in the two networks. The 4-6-4-1 and 4-8-5-1 neurons configuration were employed for the prediction model of MOE and MOR, respectively. For the prediction model of MOE, the tangent sigmoid function (Eq. 4) was chosen as transfer function between the input layer and hidden layer 1 and the linear function was used among other layers. For MOR prediction model, the linear function was just considered as the transfer function between the output layer and hidden layer 2, but the tangent sigmoid function was frequently used. The Levenberg-Marquardt backpropagation algorithm was considered as the training algorithm in both of the two models. 2
f (x )
1 e ( 2 x )
1 (4)
where f(x) is the output value of the neuron; and x is the input value of the neuron. To improve the working of the transfer functions the input and output data were normalized, which produced an output in the interval (-1, 1) and improved the ability of the network to generalization. The measured data used in the ANN models were randomly divided into three groups without repetitions, namely, the train set (90 test specimens, 60% of the total), the validation set (30 test specimens, 20% of the total) and the test set (30 test specimens, 20% of the total) for predicting MOE and MOR. The MSE and R2 were used to assess the validity of the developed ANN models. The lower MSE represented the best prediction results, and the higher R2 indicated the larger approximation between predicted and measured values. The MSE and R2 values were calculated using Eq. (5) and (6), respectively.
MSE
1
N
N
(ti i
tp i )2 (5)
1
i (ti tp i ) N i (ti t ) N
R
2
1
2
1
2
(6)
1
where ti is the measured values, tpi is the predicted values, N is the total number of data and 𝑡 is the average of predicted values.
5
Figure 1. Configuration of the ANN prediction model for MOE (a) and MOR (b)
3 Results and Discussion 3.1 Effect of HT on wood color change As shown in Figure 2a, the visual inspection of HT samples shows that the radiata pine wood becomes darker with increasing HT temperature. The value of E and C* for different HT groups is depicted in Figure 2b. The E represents the color difference, the larger the value, the greater the color difference between the HT samples and the control. The C* donates the chromatic saturation, the higher the value, the greater the color bright and purity. As seen in Figure 2b, the E enhanced noticeably with increasing HT temperature, the lowest E was seen about 11 in 185°C HT group and the highest E was 33 for 230°C HT group. The highest C* value was about 21 in 185°C HT group, then decreased slowly to 19 for 215°C HT group, and a large reduction was visible in 230°C HT group with C* value lower than 16. This phenomenon demonstrated that there was a clear correlation between the color data and HT temperature.
Figure 2. Influence of HT on wood color variation (a), E and C* values (b) As illustrated in Table 2, The L* value of wood samples decreased with the 6
risen of HT temperature, it had a mean value of 74.1 (±2.05 SD) in the control, while the L* mean value decreased to 40.62 (±1.68 SD) for 230°C HT group. The mean value of a* increased from 5.31 (±0.77 SD) in the control to 9.24 (±0.77 SD) in 215°C HT group and further decreased to 8.63 (±0.47 SD) for 230°C HT group. The mean value of b* increased from 17.59 (±0.49 SD) in the control up to 19.5 (±0.49 SD) in 185°C HT group, thereafter it decreased with increasing HT temperature and to 13.01 (±0.77 SD) for 230°C HT group. The results are consistent with the reported literature about the color change for HT samples of western hemlock wood [31]. The color change in HT samples is mainly related to chromophoric groups and auxochrome groups, which is existed in lignin, flavone and phenols. The increased redness and yellow is attributed to the degradation, condensation and oxidation reactions of lignin during HT process, which produces new colored substances, resulting in the changes of chromophoric groups [32]. While the decrease of a* value in 230°C HT group is interpreted as the volatilization of the phenolic compounds [33], and the lower b* value may be due to the evaporation of low-molecular-weight phenols in lignin origin [34].
Table 2 Color parameters of radiata pine at different HT temperatures L*
a*
b*
Temperature (°C)
Mean
SD
Mean
SD
Mean
SD
Control
74.10
2.05
5.31
0.77
17.59
0.49
185
63.54
2.16
7.45
0.48
19.50
0.49
200
56.35
2.94
8.33
0.36
18.28
0.84
215
49.23
1.63
9.24
0.31
16.55
0.82
230
40.62
1.68
8.63
0.47
13.01
0.77
SD donates standard deviation.
3.2 Effect of HT on MOE and MOR The influence of HT on MOE is depicted in Figure 3a. As can be seen, the MOE changed with increasing HT temperature in a fluctuating tendency, the highest MOE with a mean of 11 GPa was observed in 200°C HT group, and afterwards it decreased with increasing HT temperature, the lowest MOE with a mean of 9.5 GPa was found in 230°C HT group. The results are compatible with the findings of previous literature, considered that the MOE seemed to increase for moderate HT level and to decrease for more severe HT temperature [35].The highest MOE for 200°C HT group is ascribed to the reduction of cellulose and hemicelluloses, resulting in the increase of lignin content and further improving the stiffness of wood to a certain extent [36]. The MOR changed with increasing HT temperature is shown in Figure 3b, which deteriorated with increasing HT temperature except for 200°C HT group as a whole. For 185°C HT group, the MOR was decreased by about 20 MPa as compared to the control, this is interpreted as a result of hemicelluloses 7
degradation, which is less resistant to high temperature than cellulose and lignin [37-39]. It is well known that hemicelluloses have an important effect on mechanical properties of HT wood. A slight improvement for MOR with a mean value about 65 MPa was obtained in 200°C HT group compared to that of 180°C HT group, thereafter it decreased significantly with increasing HT intensity. As illustrated in Figure 3b, the mean value of MOR was about 50 MPa and 40 MPa for 215°C and 230°C HT group, respectively. The reduction of MOR when HT temperature higher than 200°C can be explained by the degradation of lignin.
Figure 3. Influence of HT on MOE (a) and MOR (b) in radiata pine wood 3.3 Prediction of MOE and MOR by ANNs The mean squared error (MSE) was employed to evaluate the performance of the ANN models developed in this study. The MSE decreased with increasing iterations in the prediction of MOE and MOR for train, validation and test data sets as shown in Figure 4a and Figure 4b, respectively. The MSE of the validation data sets in the two ANN models were all approached to 0 value, indicated that the designed ANN models had a good prediction performance. The best validation performance was 0.054921 at epoch 37 for prediction of MOE model. And for MOR, the best validation performance was 0.17423 at epoch 50. These levels of errors were satisfactory using ANN models for predicting MOE and MOR.
8
Figure 4. MSE changes with iterations for MOE (a) and MOR (b) The regression fit between measured and predicted values for MOE and MOR, in conjunction with the correlation coefficient (R) and regression equations are depicted in Figure 5. As shown, there were significant correlations between measured and predicted values in all data sets. For the prediction model of MOE, the R value for train, validation and test data sets were 0.975, 0.968 and 0.951, respectively. The R value obtained from MOR predicting model was a little smaller compared to those obtained from MOE predicting model. As can be seen, the R value in train, validation and test data sets were 0.937, 0.898 and 0.885, respectively. In addition, the determination coefficient (R2) between the measured and predicted values is also an important indicator to certify the reliability of the established ANN model. The R2 values were greater than 0.904 in all data sets for the prediction model of MOE, indicating that the established network model is capable to explain more than 90.4% of the measured values. And for the prediction model of MOR, the R2 values in train, validation and test data sets were 0.878, 0.806 and 0.783, respectively. This result demonstrated the prediction model for MOR achieved more than 78.3% success.
Figure 5. Relationship between measured and predicted values for MOE (a) and MOR (b) The comparison of measured and predicted values for MOE and MOR using ANN are illustrated in Figure 6. As shown, the MOE values calculated by utilizing ANN prediction model were very closed to the measured values by experimental 9
measurements, however the predicted values were slightly higher than measured values in all HT temperature. As for the prediction results of MOR by ANN model, the predicted values showed a forgivable deviation from the measured values. The largest deviation of about 10 MPa was observed in 185°C HT group, and the deviation was less than 5 MPa for other HT groups. These results demonstrated that the ANN prediction model had reliability after properly training, especially for the prediction of MOE. In view of this, it provided a new approach to investigate the changes of mechanical properties in HT wood with different HT temperature and duration in a non-destructive, swift and inexpensive way. For wood industry, the MOE and MOR in HT wood can be quantitatively evaluated by the change of chromatic values.
Figure 6. Comparison of measured and predicted values of MOE (a) and MOR (b)
Conclusion This study developed ANN models to predict MOE and MOR based on the changes of chromatic values. In summary, the radiata pine wood became darker with increasing HT temperature. The MOE changed with HT temperature in a fluctuating tendency, and the MOR generally deteriorated with increasing HT temperature. In the established ANN models, the predicted values were in good agreement with the measured ones, the R2 of the prediction model for MOE and MOR were 0.904 and 0.783, respectively. This demonstrate that the ANNs are appropriate for modeling the relations between chromatic values and mechanical properties in HT wood. Thus, the prediction of MOE and MOR by ANN models based on the changes of chromatic values is a suitable method to assess mechanical properties of HT wood in a non-destructive, swift and inexpensive way.
Conflict of interest 10
None.
Acknowledgement This work was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of Chinese Academy of Forestry (grant number: CAFYBB2017ZC003).
References [1] Hakkou, M., Pétrissans, M., El Bakali, I., Gérardin, P., Zoulalian, A. (2005). Wettability changes and mass loss during heat treatment of wood. Holzforschung, 59(1), 35-37. [2] Šušteršic, Ž., Mohareb, A., Chaouch, M., Pétrissans, M., Petrič, M., Gérardin, P. (2010). Prediction of the decay resistance of heat treated wood on the basis of its elemental composition. Polymer Degradation and Stability, 95(1), 94-97. [3] Herrera-Díaz, R., Sepúlveda-Villarroel, V., Pérez-Peña, N., Salvo-Sepúlveda, L., Salinas-Lira, C., Llano-Ponte, R., Ananías, R. A. (2018). Effect of wood drying and heat modification on some physical and mechanical properties of radiata pine. Drying technology, 36(5), 537-544. [4] He, Z., Wang, Z., Qu, L., Qian, J., Yi, S. (2019). Modeling and simulation of heat-mass transfer and its application in wood thermal modification. Results in Physics, 13, 102213. [5] Gunduz, G., Aydemir, D., Karakas, G. (2009). The effects of thermal treatment on the mechanical properties of wild Pear (Pyrus elaeagnifolia Pall.) wood and changes in physical properties. Materials & Design, 30(10), 4391-4395. [6] Tiryaki, S. (2015). Investigating the relationship between some mechanical properties and weight loss in heat treated woods. Journal of polytechnic-politeknik dergisi, 18(3), 149-154. [7] Borůvka, V., Zeidler, A., Holeček, T., Dudík, R. (2018). Elastic and strength properties of heat-treated Beech and Birch wood. Forests, 9(4), 197. [8] Bekhta, P., Niemz, P. (2003). Effect of high temperature on the change in color, dimensional stability and mechanical properties of spruce wood. Holzforschung, 57(5), 539-546. [9] Johansson, D., Morén, T. (2006). The potential of colour measurement for strength prediction of thermally treated wood. Holz als Roh-und Werkstoff, 64(2), 104-110. [10] Brischke, C., Welzbacher, C. R., Brandt, K., Rapp, A. O. (2007). Quality control of thermally modified timber: Interrelationship between heat treatment intensities and CIE L* a* b* color data on homogenized wood samples. Holzforschung, 61(1), 19-22. [11] Shi, J. L., Kocaefe, D., Zhang, J. (2007). Mechanical behaviour of Quebec wood species heattreated using ThermoWood process. Holz als Roh-und Werkstoff, 65(4), 255-259. [12] Percin, O., Peker, H., Atilgan, A. (2016). The effect of heat treatment on the some physical and mechanical properties of beech (Fagus orientalis lipsky) wood. Wood Research, 61(3), 443-456. [13] Kocaefe, D., Poncsak, S., Tang, J., Bouazara, M. (2010). Effect of heat treatment on the mechanical properties of North American jack pine: thermogravimetric study. Journal of materials science, 45(3), 681-687. [14] Avramidis, S., Iliadis, L. (2005). Predicting wood thermal conductivity using artificial neural networks. Wood and Fiber Science, 37(4), 682-690. [15] Avramidis, S., Iliadis, L., Mansfield, S. D. (2006). Wood dielectric loss factor prediction 11
with artificial neural networks. Wood science and technology, 40(7), 563-574 [16] Iliadis, L., Mansfield, S. D., Avramidis, S., El-Kassaby, Y. A. (2013). Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information. Holzforschung, 67(7), 771-777. [17] Ozsahin, S., Murat, M. (2018). Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks. European journal of wood and wood products, 76(2), 563-572. [18] Watanabe, K., Matsushita, Y., Kobayashi, I., Kuroda, N. (2013). Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying. Journal of wood science, 59(2), 112-118. [19] Fu, Z., Avramidis, S., Zhao, J., Cai, Y. (2017). Artificial neural network modeling for predicting elastic strain of white birch disks during drying. European journal of wood and wood products, 75(6), 949-955. [20] Fu, Z., Avramidis, S., Zhao, J., Cai, Y., Zhou, Y. (2019). Effects of saturated vapor presteaming on drying strain in Asian White Birch: Experimentation and modelling. Maderas. Ciencia y tecnología, 21(1): 77-78. [21] Fernández, F. G., de Palacios, P., Esteban, L. G., Garcia-Iruela, A., Rodrigo, B. G., Menasalvas, E. (2012). Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model. Composites Part B: Engineering, 43(8), 3528-3533. [22] Tiryaki, S., Aydın, A. (2014). An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62, 102-108. [23] Tiryaki, S., Hamzacebi, C. (2014). Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement, 49, 266274. [24] GB/T 1929-2009, Method of sample logs sawing and test specimens selection for physical and mechanical tests of wood, Standardization Administration of the People's Republic of China, Beijing, 2009. [25] González-Peña, M. M., Hale, M. D. (2009). Colour in thermally modified wood of beech, Norway spruce and Scots pine. Part 1: Colour evolution and colour changes. Holzforschung, 63(4), 385-393. [26] Liu, R., Pang, X., Yang, Z. (2017). Measurement of three wood materials against weathering during long natural sunlight exposure. Measurement, 102, 179-185. [27] GB/T 1936.2-2009, Method for determination of the modulus of elasticity in static bending of wood, General Administration of Quality Supervision, Standardization Administration of the People's Republic of China, Beijing, 2009. [28] GB/T 1936.1-2009, Method of testing in bending strength of wood, Standardization Administration of the People's Republic of China, Beijing, 2009. [29] Aghbashlo, M., Hosseinpour, S., Mujumdar, A. S. (2015). Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying technology, 33(12), 1397-1462. [30] Zhang, Y., Chen, H., Yang, B., Fu, S., Yu, J., Wang, Z. (2018). Prediction of phosphate concentrate grade based on artificial neural network modeling. Results in Physics, 11, 625-628. 12
[31] Nasir, V., Nourian, S., Avramidis, S., Cool, J. (2019). Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling” (GMDH) neural network. Holzforschung, 73(4), 381-392. [32] Ding, T., Peng, W.W., Li, T. (2017). Mechanism of color change of heat-treated white ash wood by means of FT-IR and XPS analyses. Journal of forestry engineering, 2(5), 25-30. [33] Gonzalez de Cademartori, P. H., Schneid, E., Gatto, D. A., Martins Stangerlin, D., Beltrame, R. (2013). Thermal modification of Eucalyptus grandis wood: variation of colorimetric parameters. Maderas. Ciencia y tecnología, 15(1), 57-64. [34] Hiltunen, E., Pakkanen, T. T., Alvila, L. (2006). Phenolic compounds in silver birch (Betula pendula Roth) wood. Holzforschung, 60(5), 519-527. [35] Esteves, B., Pereira, H. (2008). Wood modification by heat treatment: A review. BioResources, 4(1), 370-404. [36] Gu, L., Ding, T., Jiang, N. (2019). Development of wood heat treatment research and industrialization. Journal of forestry engineering, 4(4), 1-11. [37] Gunduz, G., Aydemir, D., Karakas, G. (2009). The effects of thermal treatment on the mechanical properties of wild Pear (Pyrus elaeagnifolia Pall.) wood and changes in physical properties. Materials & Design, 30(10), 4391-4395. [38] Korkut, S., Hiziroglu, S. (2009). Effect of heat treatment on mechanical properties of hazelnut wood (Corylus colurna L.). Materials & Design, 30(5), 1853-1858. [39] Tomak, E. D., Ustaomer, D., Yildiz, S., Pesman, E. (2014). Changes in surface and mechanical properties of heat treated wood during natural weathering. Measurement, 53, 3039.
Declarations of interest: none
Highlights 1. Changes of chromatic values in heat treatment wood are systematically discussed. 2. Variation of modulus of elasticity and modulus of rupture with heat treatment temperature are explored. 3. Artificial neural networks are employed to predict mechanical properties based on the chromatic values.
13
S0263-2241(19)31081-4 https://doi.org/10.1016/j.measurement.2019.107215 MEASUR 107215
To appear in:
Measurement
Received Date: Revised Date: Accepted Date:
22 July 2019 25 September 2019 25 October 2019
Please cite this article as: Z. Fu, F. Zhou, X. Gao, X. Weng, Y. Zhou, Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood, Measurement (2019), doi: https://doi.org/10.1016/ j.measurement.2019.107215
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier Ltd.
Assessment of mechanical properties based on the changes of chromatic values in heat treatment wood Zongying Fu, Fan Zhou, Xin Gao, Xiang Weng, Yongdong Zhou* Research Institute of Wood Industry, Chinese Academy of Forestry; Key Lab of Wood Science and Technology of Status Forestry Administration, Beijing 150040, P.R. China
Abstract In this study, the modulus of elasticity (MOE) and modulus of rupture (MOR) of heat treatment (HT) radiata pine wood were assessed by artificial neural network (ANN) models based on the changes of chromatic values. In the established ANN models, the chromatic values, i.e. L*, a* and b* conjunction with HT temperature were as input variables, the MOE and MOR as output parameters. According to the results, the values of L*, a* and b* had a good consistency with HT intensity. The MOE changed with HT temperature in a fluctuating tendency, whereas the MOR generally deteriorated with increasing HT temperature. As for the established ANN models, the predicted values were in good agreement with the measured ones, the determination coefficient of the prediction model for MOE and MOR were 0.904 and 0.783, respectively.
Keywords Artificial neural networks; chromatic values; heat treatment wood; modulus of elasticity; modulus of rupture
Corresponding author: Yongdong Zhou, Research Institute of Wood Industry, Chinese Academy of Forestry; Key Lab of Wood Science and Technology of Status Forestry Administration, Beijing, 100091, P.R. China; E-mail: [email protected]
1 Introduction As an environmentally friendly modification method, heat treatment (HT) has been widely used in wood industry. The underlying principle of HT is the thermal degradation and molecular reconstruction of wood cell wall matter, especially for the hemicelluloses with low molecular weight. It is reported that HT had some superiority in improving wood properties, such as decreasing wood hygroscopicity, improving wood dimensional stability as well as increasing biological durability [1-4]. However, when wood is exposed to a high temperature for a long duration, it becomes more brittle and the certain mechanical properties of wood will be lower because of the thermal degradation and weight losses [5-7]. 1
In addition, the color changes are also one of the important features for HT wood, which are closely related to HT temperature and duration. Several studies found that there are strong correlations between total color difference and mechanical properties in HT wood. Thus, the color parameters can be estimated quantitatively and used as a prediction of wood strength [8-10]. Many studies have been carried out on the changes of mechanical properties in HT wood. Shi et al. [11] studied the mechanical properties of spruce, pine, fir, aspen and birch heat-treated at temperatures of 200 °C or higher. As a result, the MOR decreased by 0-49% for spruce, pine, fir and aspen but for birch the MOR increased slightly. And the MOE of spruce and pine decreased between 4-28 %, however, for fir, aspen and birch the MOE increased. The samples of beech wood were heat-treated at 150, 175, and 200 °C for 1, 3 and 5 h by Percin et al. [12], which found that a small increase was determined in the MOR, MOE and Brinell hardness at 150 °C for 1 and 3 h. However, all of them were clearly decreased at higher temperature and duration. Kocaefe et al. [13] reported that the mechanical properties (MOE, MOR, hardness, etc.) of HT jack pine wood generally deteriorated at HT temperatures higher than 200 °C compared to those of untreated wood. From the results of above literatures, the effects of HT on mechanical properties were various at HT temperatures below than 200 °C, but for HT temperatures above than 200 °C, most of the mechanical properties in HT wood clearly decreased, and there was some difference in reduction level for different wood species, HT temperature and duration. To obtain the changes of mechanical properties for different HT process, a large number of comprehensive experiments with procedural difficulties and long times need to be carried out, thus it is necessary to explore suitable modeling methods for the investigation of mechanical properties in HT wood. As a fascinating mathematical tool without assumptions and knowing a priori the model structure, artificial neural networks (ANNs) have been widely used in the field of wood evaluation and processing. The thermal conductivity [14], dielectric loss factor [15], density [16], equilibrium moisture content [17] of wood have been predicted by different ANN models. Watanabe et al. [18] evaluated the final moisture content of sugi wood samples after air-drying by an ANN model according to initial moisture content, basic density, heartwood ratio, lightness, annual ring orientation and width. Fu et al. [19, 20] developed three layers feedforward perception network models to predict elastic strain and mechanosorptive creep of white birch disks during conventional drying. In addition, there are also a few studies on predicting MOE and MOR by ANN models. The prediction of MOE and MOR of structural plywood board by ANN was reported by Fernández et al. [21], which demonstrated the high capacity of ANN for modelling complex relations between variables. The wood species, HT temperature and time were considered as the input variables to predict MOE and MOR of HT woods have been reported by Tiryaki et al., who also developed an ANN model to predict compression strength of HT wood parallel to grain, indicated that the ANN model provided high prediction precision compared with multiple linear regression 2
model [22, 23]. In this study, an ANN model was established to predict MOE and MOR in HT radiata pine wood, considering chromatic values, i.e. lightness (L*), red/green (a*), blue/yellow (b*) conjunction with HT temperature as input variables. The main objective of this research is to predict MOE and MOR values of HT wood with different HT temperature and duration in a non-destructive, swift and inexpensive way.
2 Materials and Methods 2.1 Materials Radiata pine (Pinups radiata D. Don) lumbers were imported from New Zealand with the diameter above 50 cm at breast height and the basic density of 0.45 g/cm3. The lumbers were sawed into boards of 25 mm in thickness and dried to average moisture content about 12% in Shandong Lichen Group Co., LTD. (Shandong province, China). The boards without visible defects were picked out and sawed into HT specimens with the dimensions of 500 mm × 120 mm × 25 mm (Longitudinal × Tangential × Radial, L × T × R) for HT experiment and color measurement. Afterwards, the wood specimens were cut into the test samples for MOE and MOR measurement with dimensions of 300 mm × 20 mm × 20 mm (L × T × R) according to GB/T 1929-2009 [24]. 2.2 HT experiments The HT experiments were conducted in a self-designed laboratory HT equipment at atmospheric pressure, in which superheated steam as shielding gas. Four different temperatures (185, 200, 215 and 230 °C) and a certain duration of 4 h were applied to wood specimens. Thus, wood specimens were randomly divided into four HT groups of having twenty specimens in each, and the nontreated wood specimens as the control of twenty specimens were also considered for the following tests. The HT process included pre-heating period, drying period, temperature rise period, HT period and temperature fall period (Table 1). For each period, the oxygen concentration was monitored by a transducer and controlled by changing the water vapor content. Table 1 The process design phases for HT experiment Process phase
Temperature (°C)
Oxygen concentration (%)
Time (h)
Pre-heating Drying Temperature rise HT Temperature fall
100 130 180 185/200/215/230 100
16 13 5 2 16
1 2 1 4 1
2.3 Surface color measurements of HT wood 3
The three-dimensional CIE L*a*b* color space system was employed in this test [25, 26]. Six marked points on the surface of each specimen were measured to obtain an objective characterization of the specimen color by a tristimulus color meter CR-400 (Konica Minolta (China) Investment LTD.). The values of vertical coordinate L*, the chromatic coordinates a* and b*were recorded. After measuring the color coordinates, the total color change ∆E and C* was calculated using Eq. (1) and (2), respectively.
E (L* )2 (a * )2 (b * )2 (1)
C * (a * )2 (b * )2 (2) where ∆E is the total color difference, ∆L* is the difference in lightness between the control and HT groups, ∆a* and ∆b* are the difference in chromatic coordinates between the control and HT groups, and C* is the chromatic saturation. 2.4 Mechanical properties test of HT wood The MOE and MOR were determined by universal mechanical testing machine. In each HT temperature level, thirty standard samples for mechanical properties with dimension of 300 mm × 20 mm × 20 mm (L × T × R) were used to the test. The loading was done at the tangential directions of samples. Prior to mechanical properties test, the moisture content of test samples was balanced to 12% in a conditioning chamber. The MOE was measured firstly according to GB/T 1936.2-2009 [27], and thereafter MOR was measured based on GB/T 1936.1-2009 [28]. After determining MOE and MOR, the moisture content of the test samples was measured, and transformed the mechanical strength value to 12% moisture content using Eq. (3).
12 m 1 M 12
(3)
where δ12 is the strength value at moisture content of 12%, δm is the strength value at the moisture content of M, is the constant value showing relationship between strength and moisture content, M is the moisture content of the test samples during the mechanical properties test. 2.5 ANN model Inspired by the functional behavior of the biological nervous system, ANN modeling is particularly useful for dealing with the nonlinearities and complexities of ill-defined processes using past historical data, even if all mechanisms and principles are not clarified; further, the network can be built directly from experimental data by the self-organizing capabilities without any prior assumptions [29, 30]. In general, the basic information processing elements of the neural network operation are called neurons. Some of the neurons interface with the outside world to receive information called input layer, and other neurons communicate the prediction to the outside world called output layer. All the rest of the neurons connect the input layer to the output layer called hidden 4
layers. The network function is determined largely by the interconnections between neurons, which are not simple connections but some non-linear functions [19]. The Back-Propagation neural networks were used in this study. As shown in Figure 1, the HT temperature, chromatic values including L*, a* and b* were considered as input variables, the MOE and MOR were used as outputs in the two networks. The 4-6-4-1 and 4-8-5-1 neurons configuration were employed for the prediction model of MOE and MOR, respectively. For the prediction model of MOE, the tangent sigmoid function (Eq. 4) was chosen as transfer function between the input layer and hidden layer 1 and the linear function was used among other layers. For MOR prediction model, the linear function was just considered as the transfer function between the output layer and hidden layer 2, but the tangent sigmoid function was frequently used. The Levenberg-Marquardt backpropagation algorithm was considered as the training algorithm in both of the two models. 2
f (x )
1 e ( 2 x )
1 (4)
where f(x) is the output value of the neuron; and x is the input value of the neuron. To improve the working of the transfer functions the input and output data were normalized, which produced an output in the interval (-1, 1) and improved the ability of the network to generalization. The measured data used in the ANN models were randomly divided into three groups without repetitions, namely, the train set (90 test specimens, 60% of the total), the validation set (30 test specimens, 20% of the total) and the test set (30 test specimens, 20% of the total) for predicting MOE and MOR. The MSE and R2 were used to assess the validity of the developed ANN models. The lower MSE represented the best prediction results, and the higher R2 indicated the larger approximation between predicted and measured values. The MSE and R2 values were calculated using Eq. (5) and (6), respectively.
MSE
1
N
N
(ti i
tp i )2 (5)
1
i (ti tp i ) N i (ti t ) N
R
2
1
2
1
2
(6)
1
where ti is the measured values, tpi is the predicted values, N is the total number of data and 𝑡 is the average of predicted values.
5
Figure 1. Configuration of the ANN prediction model for MOE (a) and MOR (b)
3 Results and Discussion 3.1 Effect of HT on wood color change As shown in Figure 2a, the visual inspection of HT samples shows that the radiata pine wood becomes darker with increasing HT temperature. The value of E and C* for different HT groups is depicted in Figure 2b. The E represents the color difference, the larger the value, the greater the color difference between the HT samples and the control. The C* donates the chromatic saturation, the higher the value, the greater the color bright and purity. As seen in Figure 2b, the E enhanced noticeably with increasing HT temperature, the lowest E was seen about 11 in 185°C HT group and the highest E was 33 for 230°C HT group. The highest C* value was about 21 in 185°C HT group, then decreased slowly to 19 for 215°C HT group, and a large reduction was visible in 230°C HT group with C* value lower than 16. This phenomenon demonstrated that there was a clear correlation between the color data and HT temperature.
Figure 2. Influence of HT on wood color variation (a), E and C* values (b) As illustrated in Table 2, The L* value of wood samples decreased with the 6
risen of HT temperature, it had a mean value of 74.1 (±2.05 SD) in the control, while the L* mean value decreased to 40.62 (±1.68 SD) for 230°C HT group. The mean value of a* increased from 5.31 (±0.77 SD) in the control to 9.24 (±0.77 SD) in 215°C HT group and further decreased to 8.63 (±0.47 SD) for 230°C HT group. The mean value of b* increased from 17.59 (±0.49 SD) in the control up to 19.5 (±0.49 SD) in 185°C HT group, thereafter it decreased with increasing HT temperature and to 13.01 (±0.77 SD) for 230°C HT group. The results are consistent with the reported literature about the color change for HT samples of western hemlock wood [31]. The color change in HT samples is mainly related to chromophoric groups and auxochrome groups, which is existed in lignin, flavone and phenols. The increased redness and yellow is attributed to the degradation, condensation and oxidation reactions of lignin during HT process, which produces new colored substances, resulting in the changes of chromophoric groups [32]. While the decrease of a* value in 230°C HT group is interpreted as the volatilization of the phenolic compounds [33], and the lower b* value may be due to the evaporation of low-molecular-weight phenols in lignin origin [34].
Table 2 Color parameters of radiata pine at different HT temperatures L*
a*
b*
Temperature (°C)
Mean
SD
Mean
SD
Mean
SD
Control
74.10
2.05
5.31
0.77
17.59
0.49
185
63.54
2.16
7.45
0.48
19.50
0.49
200
56.35
2.94
8.33
0.36
18.28
0.84
215
49.23
1.63
9.24
0.31
16.55
0.82
230
40.62
1.68
8.63
0.47
13.01
0.77
SD donates standard deviation.
3.2 Effect of HT on MOE and MOR The influence of HT on MOE is depicted in Figure 3a. As can be seen, the MOE changed with increasing HT temperature in a fluctuating tendency, the highest MOE with a mean of 11 GPa was observed in 200°C HT group, and afterwards it decreased with increasing HT temperature, the lowest MOE with a mean of 9.5 GPa was found in 230°C HT group. The results are compatible with the findings of previous literature, considered that the MOE seemed to increase for moderate HT level and to decrease for more severe HT temperature [35].The highest MOE for 200°C HT group is ascribed to the reduction of cellulose and hemicelluloses, resulting in the increase of lignin content and further improving the stiffness of wood to a certain extent [36]. The MOR changed with increasing HT temperature is shown in Figure 3b, which deteriorated with increasing HT temperature except for 200°C HT group as a whole. For 185°C HT group, the MOR was decreased by about 20 MPa as compared to the control, this is interpreted as a result of hemicelluloses 7
degradation, which is less resistant to high temperature than cellulose and lignin [37-39]. It is well known that hemicelluloses have an important effect on mechanical properties of HT wood. A slight improvement for MOR with a mean value about 65 MPa was obtained in 200°C HT group compared to that of 180°C HT group, thereafter it decreased significantly with increasing HT intensity. As illustrated in Figure 3b, the mean value of MOR was about 50 MPa and 40 MPa for 215°C and 230°C HT group, respectively. The reduction of MOR when HT temperature higher than 200°C can be explained by the degradation of lignin.
Figure 3. Influence of HT on MOE (a) and MOR (b) in radiata pine wood 3.3 Prediction of MOE and MOR by ANNs The mean squared error (MSE) was employed to evaluate the performance of the ANN models developed in this study. The MSE decreased with increasing iterations in the prediction of MOE and MOR for train, validation and test data sets as shown in Figure 4a and Figure 4b, respectively. The MSE of the validation data sets in the two ANN models were all approached to 0 value, indicated that the designed ANN models had a good prediction performance. The best validation performance was 0.054921 at epoch 37 for prediction of MOE model. And for MOR, the best validation performance was 0.17423 at epoch 50. These levels of errors were satisfactory using ANN models for predicting MOE and MOR.
8
Figure 4. MSE changes with iterations for MOE (a) and MOR (b) The regression fit between measured and predicted values for MOE and MOR, in conjunction with the correlation coefficient (R) and regression equations are depicted in Figure 5. As shown, there were significant correlations between measured and predicted values in all data sets. For the prediction model of MOE, the R value for train, validation and test data sets were 0.975, 0.968 and 0.951, respectively. The R value obtained from MOR predicting model was a little smaller compared to those obtained from MOE predicting model. As can be seen, the R value in train, validation and test data sets were 0.937, 0.898 and 0.885, respectively. In addition, the determination coefficient (R2) between the measured and predicted values is also an important indicator to certify the reliability of the established ANN model. The R2 values were greater than 0.904 in all data sets for the prediction model of MOE, indicating that the established network model is capable to explain more than 90.4% of the measured values. And for the prediction model of MOR, the R2 values in train, validation and test data sets were 0.878, 0.806 and 0.783, respectively. This result demonstrated the prediction model for MOR achieved more than 78.3% success.
Figure 5. Relationship between measured and predicted values for MOE (a) and MOR (b) The comparison of measured and predicted values for MOE and MOR using ANN are illustrated in Figure 6. As shown, the MOE values calculated by utilizing ANN prediction model were very closed to the measured values by experimental 9
measurements, however the predicted values were slightly higher than measured values in all HT temperature. As for the prediction results of MOR by ANN model, the predicted values showed a forgivable deviation from the measured values. The largest deviation of about 10 MPa was observed in 185°C HT group, and the deviation was less than 5 MPa for other HT groups. These results demonstrated that the ANN prediction model had reliability after properly training, especially for the prediction of MOE. In view of this, it provided a new approach to investigate the changes of mechanical properties in HT wood with different HT temperature and duration in a non-destructive, swift and inexpensive way. For wood industry, the MOE and MOR in HT wood can be quantitatively evaluated by the change of chromatic values.
Figure 6. Comparison of measured and predicted values of MOE (a) and MOR (b)
Conclusion This study developed ANN models to predict MOE and MOR based on the changes of chromatic values. In summary, the radiata pine wood became darker with increasing HT temperature. The MOE changed with HT temperature in a fluctuating tendency, and the MOR generally deteriorated with increasing HT temperature. In the established ANN models, the predicted values were in good agreement with the measured ones, the R2 of the prediction model for MOE and MOR were 0.904 and 0.783, respectively. This demonstrate that the ANNs are appropriate for modeling the relations between chromatic values and mechanical properties in HT wood. Thus, the prediction of MOE and MOR by ANN models based on the changes of chromatic values is a suitable method to assess mechanical properties of HT wood in a non-destructive, swift and inexpensive way.
Conflict of interest 10
None.
Acknowledgement This work was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of Chinese Academy of Forestry (grant number: CAFYBB2017ZC003).
References [1] Hakkou, M., Pétrissans, M., El Bakali, I., Gérardin, P., Zoulalian, A. (2005). Wettability changes and mass loss during heat treatment of wood. Holzforschung, 59(1), 35-37. [2] Šušteršic, Ž., Mohareb, A., Chaouch, M., Pétrissans, M., Petrič, M., Gérardin, P. (2010). Prediction of the decay resistance of heat treated wood on the basis of its elemental composition. Polymer Degradation and Stability, 95(1), 94-97. [3] Herrera-Díaz, R., Sepúlveda-Villarroel, V., Pérez-Peña, N., Salvo-Sepúlveda, L., Salinas-Lira, C., Llano-Ponte, R., Ananías, R. A. (2018). Effect of wood drying and heat modification on some physical and mechanical properties of radiata pine. Drying technology, 36(5), 537-544. [4] He, Z., Wang, Z., Qu, L., Qian, J., Yi, S. (2019). Modeling and simulation of heat-mass transfer and its application in wood thermal modification. Results in Physics, 13, 102213. [5] Gunduz, G., Aydemir, D., Karakas, G. (2009). The effects of thermal treatment on the mechanical properties of wild Pear (Pyrus elaeagnifolia Pall.) wood and changes in physical properties. Materials & Design, 30(10), 4391-4395. [6] Tiryaki, S. (2015). Investigating the relationship between some mechanical properties and weight loss in heat treated woods. Journal of polytechnic-politeknik dergisi, 18(3), 149-154. [7] Borůvka, V., Zeidler, A., Holeček, T., Dudík, R. (2018). Elastic and strength properties of heat-treated Beech and Birch wood. Forests, 9(4), 197. [8] Bekhta, P., Niemz, P. (2003). Effect of high temperature on the change in color, dimensional stability and mechanical properties of spruce wood. Holzforschung, 57(5), 539-546. [9] Johansson, D., Morén, T. (2006). The potential of colour measurement for strength prediction of thermally treated wood. Holz als Roh-und Werkstoff, 64(2), 104-110. [10] Brischke, C., Welzbacher, C. R., Brandt, K., Rapp, A. O. (2007). Quality control of thermally modified timber: Interrelationship between heat treatment intensities and CIE L* a* b* color data on homogenized wood samples. Holzforschung, 61(1), 19-22. [11] Shi, J. L., Kocaefe, D., Zhang, J. (2007). Mechanical behaviour of Quebec wood species heattreated using ThermoWood process. Holz als Roh-und Werkstoff, 65(4), 255-259. [12] Percin, O., Peker, H., Atilgan, A. (2016). The effect of heat treatment on the some physical and mechanical properties of beech (Fagus orientalis lipsky) wood. Wood Research, 61(3), 443-456. [13] Kocaefe, D., Poncsak, S., Tang, J., Bouazara, M. (2010). Effect of heat treatment on the mechanical properties of North American jack pine: thermogravimetric study. Journal of materials science, 45(3), 681-687. [14] Avramidis, S., Iliadis, L. (2005). Predicting wood thermal conductivity using artificial neural networks. Wood and Fiber Science, 37(4), 682-690. [15] Avramidis, S., Iliadis, L., Mansfield, S. D. (2006). Wood dielectric loss factor prediction 11
with artificial neural networks. Wood science and technology, 40(7), 563-574 [16] Iliadis, L., Mansfield, S. D., Avramidis, S., El-Kassaby, Y. A. (2013). Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information. Holzforschung, 67(7), 771-777. [17] Ozsahin, S., Murat, M. (2018). Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks. European journal of wood and wood products, 76(2), 563-572. [18] Watanabe, K., Matsushita, Y., Kobayashi, I., Kuroda, N. (2013). Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying. Journal of wood science, 59(2), 112-118. [19] Fu, Z., Avramidis, S., Zhao, J., Cai, Y. (2017). Artificial neural network modeling for predicting elastic strain of white birch disks during drying. European journal of wood and wood products, 75(6), 949-955. [20] Fu, Z., Avramidis, S., Zhao, J., Cai, Y., Zhou, Y. (2019). Effects of saturated vapor presteaming on drying strain in Asian White Birch: Experimentation and modelling. Maderas. Ciencia y tecnología, 21(1): 77-78. [21] Fernández, F. G., de Palacios, P., Esteban, L. G., Garcia-Iruela, A., Rodrigo, B. G., Menasalvas, E. (2012). Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model. Composites Part B: Engineering, 43(8), 3528-3533. [22] Tiryaki, S., Aydın, A. (2014). An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62, 102-108. [23] Tiryaki, S., Hamzacebi, C. (2014). Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement, 49, 266274. [24] GB/T 1929-2009, Method of sample logs sawing and test specimens selection for physical and mechanical tests of wood, Standardization Administration of the People's Republic of China, Beijing, 2009. [25] González-Peña, M. M., Hale, M. D. (2009). Colour in thermally modified wood of beech, Norway spruce and Scots pine. Part 1: Colour evolution and colour changes. Holzforschung, 63(4), 385-393. [26] Liu, R., Pang, X., Yang, Z. (2017). Measurement of three wood materials against weathering during long natural sunlight exposure. Measurement, 102, 179-185. [27] GB/T 1936.2-2009, Method for determination of the modulus of elasticity in static bending of wood, General Administration of Quality Supervision, Standardization Administration of the People's Republic of China, Beijing, 2009. [28] GB/T 1936.1-2009, Method of testing in bending strength of wood, Standardization Administration of the People's Republic of China, Beijing, 2009. [29] Aghbashlo, M., Hosseinpour, S., Mujumdar, A. S. (2015). Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying technology, 33(12), 1397-1462. [30] Zhang, Y., Chen, H., Yang, B., Fu, S., Yu, J., Wang, Z. (2018). Prediction of phosphate concentrate grade based on artificial neural network modeling. Results in Physics, 11, 625-628. 12
[31] Nasir, V., Nourian, S., Avramidis, S., Cool, J. (2019). Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling” (GMDH) neural network. Holzforschung, 73(4), 381-392. [32] Ding, T., Peng, W.W., Li, T. (2017). Mechanism of color change of heat-treated white ash wood by means of FT-IR and XPS analyses. Journal of forestry engineering, 2(5), 25-30. [33] Gonzalez de Cademartori, P. H., Schneid, E., Gatto, D. A., Martins Stangerlin, D., Beltrame, R. (2013). Thermal modification of Eucalyptus grandis wood: variation of colorimetric parameters. Maderas. Ciencia y tecnología, 15(1), 57-64. [34] Hiltunen, E., Pakkanen, T. T., Alvila, L. (2006). Phenolic compounds in silver birch (Betula pendula Roth) wood. Holzforschung, 60(5), 519-527. [35] Esteves, B., Pereira, H. (2008). Wood modification by heat treatment: A review. BioResources, 4(1), 370-404. [36] Gu, L., Ding, T., Jiang, N. (2019). Development of wood heat treatment research and industrialization. Journal of forestry engineering, 4(4), 1-11. [37] Gunduz, G., Aydemir, D., Karakas, G. (2009). The effects of thermal treatment on the mechanical properties of wild Pear (Pyrus elaeagnifolia Pall.) wood and changes in physical properties. Materials & Design, 30(10), 4391-4395. [38] Korkut, S., Hiziroglu, S. (2009). Effect of heat treatment on mechanical properties of hazelnut wood (Corylus colurna L.). Materials & Design, 30(5), 1853-1858. [39] Tomak, E. D., Ustaomer, D., Yildiz, S., Pesman, E. (2014). Changes in surface and mechanical properties of heat treated wood during natural weathering. Measurement, 53, 3039.
Declarations of interest: none
Highlights 1. Changes of chromatic values in heat treatment wood are systematically discussed. 2. Variation of modulus of elasticity and modulus of rupture with heat treatment temperature are explored. 3. Artificial neural networks are employed to predict mechanical properties based on the chromatic values.
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