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Citation: Nazerian, M.; Naderi, F.;
Papadopoulos, A.N. Application of
the Artificial Neural Network to
Predict the Bending Strength of the
Engineered Laminated Wood
Produced Using the Hydrolyzed Soy
Protein-Melamine Urea
Formaldehyde Copolymer Adhesive.
J. Compos. Sci. 2023,7, 206. https://
doi.org/10.3390/jcs7050206
Academic Editor:
Francesco Tornabene
Received: 13 April 2023
Revised: 14 May 2023
Accepted: 18 May 2023
Published: 21 May 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Application of the Artificial Neural Network to Predict the
Bending Strength of the Engineered Laminated Wood Produced
Using the Hydrolyzed Soy Protein-Melamine Urea
Formaldehyde Copolymer Adhesive
Morteza Nazerian 1, Fatemeh Naderi 1and Antonios N. Papadopoulos 2, *
1Department of Bio Systems, Faculty of New Technologies and Aerospace Engineering,
Shahid Beheshti University, Tehran 1983969411, Iran; m_nazerian@sbu.ac.ir (M.N.);
naderi1393f@gmail.com (F.N.)
2Laboratory of Wood Chemistry and Technology, Department of Forestry and Natural Environment,
International Hellenic University, GR-661 00 Drama, Greece
*Correspondence: antpap@for.ihu.gr
Abstract:
The artificial neural network (ANN) was used to predict the modulus of rupture (MOR) of
the laminated wood products adhered by melamine/urea formaldehyde (MUF) resin with different
formaldehyde to melamine/urea molar ratios combined with different weight ratios of the protein
adhesive resulting from the alkaline treatment (NaOH) of the soybean oil meal to MUF resin pressed
at different temperatures according to the central composite design (CCD). After making the boards
and performing the mechanical test to measure the MOR, based on experimental data, different
statistics such as determination coefficient (R
2
), root mean square error (RMSE), mean absolute error
(MAE) and sum of squares error (SSE) were determined, and then the suitable algorithm was selected
to determine the estimated values. After comparing estimated values with the experimental values,
the direct and interactive effects of the independent variables on MOR were determined. The results
indicated that using suitable algorithms to train the ANN well, a very good estimate of the bending
strength of the laminated wood products can be offered with the least error. In addition, based on
the estimated and measured strengths and FTIR and TGA diagnostic analyses, it was found that the
replacement of the MUF resin by the protein bio-based adhesive when using low F to M/U molar
ratios, the MOR is maximized if a high range of temperature is used during the press.
Keywords: wood laminated product; MUF-modified protein adhesive; optimization; MOR; ANN
1. Introduction
Soy flour is the waste of the process of extracting soy bean oil. When extracting
oil, about 4.5 tons of waste flour is produced per each ton of oil [
1
] and using it can be
very useful both economically and environmentally. So far, formaldehyde-based synthetic
resins have been the main adhesive used to produce wood products while in addition
to limitations in using fossil resources, the release of formaldehyde gas has made their
limitations more evident [
2
,
3
]. Hence, the adhesives resulting from biomass waste resources
can be a suitable alternative to mitigate the negative effects on the environment and
avoid dependency on fossil fuel resources. Despite a lot of soy wastes used to feed the
livestock, a lot of them are still produced every year [
4
]. Recently, as the awareness of the
environmental effect and resource stability increases, attempts are made to use the soy flour
and other biomass resources to make bio-based formaldehyde-free adhesives to product
wood products [
5
–
9
]. To overcome the disadvantages of using the bio-based materials
such as dry and wet strengths of their products, activities are performed by applying
unsaturation process such as acid treatments [
10
] and alkaline treatment [
11
] to denaturate
the protein together with using the petrochemical crosslinkers and bio-based additives
J. Compos. Sci. 2023,7, 206. https://doi.org/10.3390/jcs7050206 https://www.mdpi.com/journal/jcs
J. Compos. Sci. 2023,7, 206 2 of 20
resulting from the wastes of different wood industries. During the denaturation, the protein
chain is uncoiled and both hydrophilic and hydrophobic exposed groups are released so
that the interaction between the protein molecules and the protein with substrate increases
during the curing [
12
]. In alkaline conditions, the moisture and mechanical properties of
the panels adhered by the protein adhesive improve so that the requirements of the strength
are met for internal consumptions. Denaturation along with crosslinkers can engage amino-
groups on the protein and improve the strength due to ester connections [
13
,
14
]. However,
using the bio-based additives such as lignin as the crosslinker also usually requires very
complicated modification techniques and increases the cost so that using petrochemical
materials as the crosslink accelerating agent becomes more cost-effective and it mitigates
the environmental issues such as the release of formaldehyde gas. For this purpose, treating
the protein chemically and activating the active groups connectable with amine-groups on
the molecule of petrochemical materials such as urea, melamine, ect, the bio-based adhesive
can be produced.
To optimize the process of producing wood laminated products, all parameters and
variables must be considered and tested. However, the artificial neural network modeling
can be used to determine the optimal process parameters to make the laminated product
without spending a lot of cost, energy and time.
ANNs are an advanced data modeling means used to model the complex undefined
nonlinear relations between the inputs and outputs without any preliminary assumptions
or mathematical relations existing between them [
15
]. They are inspired from the bio-
logical neural network computational concept. Today, ANNs are applied as one of the
most attractive branches of the artificial intelligence in many scientific and engineering
applications including prediction, optimization, classification, identification of patterns
and data processing [16].
In wood science, the latest studies have shown the capability of ANN to predict the
physical and mechanical properties of wood [
17
–
24
], wood composite products [
25
–
31
]
and wood machining [32–34].
To train the relationship between the input and output variables, the neural network
is trained together with the data related to the problem being examined through a training
algorithm. Training is the process of adjusting the connection weights directing the ANN
to produce outputs equal or close to the target values [
35
]. Meanwhile, the feed forward
and back propagation is the most famous training algorithm to train the ANN [
36
]. The
multilayer perceptron (MLP) feed forward neural network is the commonest prediction
architecture [
22
,
37
]. The MLP architecture includes one input layer, one output layer and
one or more hidden layers depending on the complexity of the problem being examined.
Each layer in MLP is composed of some interconnected elements known as “neurons” and
the neurons direct the network toward producing one special output of the input variables.
An example of the MLP architecture is given in Figure 1based on which the optimized
architecture is established.
Based on the Equation (1), the output of the MLP given in Figure 1is also computed:
Y=g θ+
m
∑
j=1
vj"n
∑
i=1
fwij xi+βj#! (1)
where Yis the predicted value of the dependent variable; x
i
is the input of the i-th inde-
pendent variable; w
ij
is the connection weight of the n-th input neuron and the i-th hidden
neuron; βiis the bias value of the i-th hidden neuron; vjis the connection weight between
the j-th hidden neuron;
θ
is the output bias value; and f(.) are the activation functions of the
output and hidden neurons, respectively.
J. Compos. Sci. 2023,7, 206 3 of 20
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 3 of 21
Figure 1. A typical multi-layered ANN architecture based on MLP.
Based on the Equation (1), the output of the MLP given in Figure 1 is also computed:
(1)
where Y is the predicted value of the dependent variable; xi is the input of the i-th inde-
pendent variable; wij is the connection weight of the n-th input neuron and the i-th hidden
neuron; βi is the bias value of the i-th hidden neuron; vj is the connection weight between
the j-th hidden neuron; θ is the output bias value; and f(.) are the activation functions of
the output and hidden neurons, respectively.
In the ANN architecture, the rst and last layers are the input and output layers, re-
spectively. The layer between the input and output layers is known as the “hidden layer”.
The hidden layer receives the data from the input layer and processes the data subse-
quently and then, it sends them as the response to the output layer. The output layer re-
ceives the resulting response from the hidden layer and produces the output data for the
network input layer. In this way, it sends the output data to the outside environment [16].
In the relevant literature, the eects of various process variables are discussed on the
bending strength of the laminated products in detail. Furthermore, ANN aempts are
made to predict the strength properties of the laminated wood products. It is determined
that there is very lile information on the possibility of using the ANN to estimate the
strength properties of the laminated wood products based on the protein bio-based adhe-
sives. Hence, the main purpose of the present research is to examine and predict the bend-
ing strength of the laminated products adhered by the plant protein adhesive resulting
from the soybean oil meal combined with the melamine-urea formaldehyde (MUF) resin
at dierent molar ratios cured at dierent press temperatures.
2. Materials and Methods
2.1. Materials
Edible soybean oil meal (produced by Khoshpak Products Co., Tehran, Iran) contain-
ing 53.3 g protein per 100 g) was used after treating it with NaOH (99%), ethylene glycol
(density 1.11 kg/L) and HCl (20%) to produce the bio-based protein adhesive. Powder
melamine (with the purity 99.8%), urea (purity 46%), formalin (density 1.08 gr/cm3, pH
2.5–4 and concentration 38%), NaOH (40%), butanol and ammonium chloride (20%) were
also used to produce melamine urea formaldehyde (MUF) resin.
Figure 1. A typical multi-layered ANN architecture based on MLP.
In the ANN architecture, the first and last layers are the input and output layers,
respectively. The layer between the input and output layers is known as the “hidden layer”.
The hidden layer receives the data from the input layer and processes the data subsequently
and then, it sends them as the response to the output layer. The output layer receives the
resulting response from the hidden layer and produces the output data for the network
input layer. In this way, it sends the output data to the outside environment [16].
In the relevant literature, the effects of various process variables are discussed on the
bending strength of the laminated products in detail. Furthermore, ANN attempts are made
to predict the strength properties of the laminated wood products. It is determined that
there is very little information on the possibility of using the ANN to estimate the strength
properties of the laminated wood products based on the protein bio-based adhesives.
Hence, the main purpose of the present research is to examine and predict the bending
strength of the laminated products adhered by the plant protein adhesive resulting from the
soybean oil meal combined with the melamine-urea formaldehyde (MUF) resin at different
molar ratios cured at different press temperatures.
2. Materials and Methods
2.1. Materials
Edible soybean oil meal (produced by Khoshpak Products Co., Tehran, Iran) containing
53.3 g protein per 100 g) was used after treating it with NaOH (99%), ethylene glycol (density
1.11 kg/L) and HCl (20%) to produce the bio-based protein adhesive. Powder melamine
(with the purity 99.8%), urea (purity 46%), formalin (density 1.08 gr/cm
3
, pH 2.5–4 and
concentration 38%), NaOH (40%), butanol and ammonium chloride (20%) were also used
to produce melamine urea formaldehyde (MUF) resin.
To make the laminated product and to examine the effect of the dependent variables
on the bending strength accurately, samples were prepared randomly from walnut wood
(Juglans regia L.) grown in the north of Iran with the average age 30 years old and average
density 700 g/cm3 after examining some wood species due to the prevention of the internal
swelling of the board due to the accumulation of the water vapor under the press. A special
emphasis was made to prepare boards from flawless logs. Boards with the dimensions
350 ×70 ×37 mm
were prepared radially. After storing them in the laboratory for 3 months
and reaching the equilibrium moisture content 15%, the samples were exposed to the
temperature 110 ◦C until the moisture content reached 8%.
2.2. Methods
2.2.1. Experimental Design
To determine the effect of the independent variables on the response (MOR) and
predict the direct, interactive and square effects of the variables on the response in the
J. Compos. Sci. 2023,7, 206 4 of 20
process of using the ANN, the experiments were designed first using the response surface
methodology (RSM) and based on the center composite design (CCD). In this process, three
distinct points including the axial, factorial and central points were distinguished in a
cubic matrix environment and the number of iterations was set at 2 per each point. The
levels of each variable including the molar ratio of formaldehyde to melamine/urea (MR),
weight ratio of the modified protein (MP) to MUF resin (WR) and the press temperature
(Tem) were 1.68:1, 1.805:1, 1.93:1, 20:80, 40:60, 60:40 and 140, 160 and 180
◦
C, respectively.
According to the number of the axial, factorial and central points and 2 iterations for each of
these points and based on the equation 2n + (n
×
2) + k where n is the number of variables
and k is the number of iterations at the cube center,34 test samples were prepared for the
bending test.
2.2.2. Preparation of the Soybean Oil Meal-Modified Protein Adhesive
Preparation of protein adhesive was done based on previous research [
38
]. For this
purpose, to the aqueous solution (333.33 mL) containing 1.766 g ethylene glycol (as the
phase transferrer) and 9.33 alkali (NaOH 99%), 116.66 g soybean oil meal was added that
had passed through the sieve with the mesh size 100 after the aqueous solution was heated
and its temperature reached 70
◦
C in a three-necked flask equipped with the mechanical
stirrer, thermometer and refrigerator while the mixer rotated with the speed 650 rpm.
As the mixture’s temperature increased to 88–90
◦
C for 15 min, the solution was kept at
this temperature for 2 h to complete the reaction. Then using the ice bath, the solution’s
temperature decreased to 35
◦
C quickly. Using HCl 20%, the mixture’s pH decreased from
12.9 to 7 and after passing through the sieve with the mesh size 35 and leach it and remove
the lumps, it was kept in the refrigerator at the temperature 3 ◦C to be used later.
2.2.3. Making the MUF Resin and Wood Laminated Product
After installing the three-necked flask equipped with the pH-meter, condenser con-
nected to the water flow and alcohol thermometer in a hot oil bath, all formaldehyde (as
formalin solution 38%) required to make each resin with different F:MU molar ratios was
loaded in the flask (according to Table 1as the design of experiment, with different values
including 202.7 g equivalent to 1.68 mol, 218.9 g equivalent to 1.805 mol and 235.12 g
equivalent to 1.93 mol) and in the first making stage (alkaline stage), the first part of urea
equivalent to 79.48 g (or 92%) was added to the formalin solution. Then, the pH of the
resulting solution (5.3–5.5) was increased to 8–8.4% by adding NaOH 40%. As the reaction’s
temperature increased to 55–66
◦
C and adding butanol in the amount of 2.5% of the total
weight of melamine + urea to the solution and all melamine (10% of total urea + melamine
equivalent to 9.08 g), the solution was heated for 30 min. In the second (acidic) stage, and
controlling the pH by adding ammonium chloride 20%, the pH decreased to 5–5.5. As the
solution’s temperature increased to 80–85
◦
C and after keeping it at this temperature for
5–10 min and ensuring the complete solution of urea and melamine through testing the
formation of a transparent solution in a cold water medium by adding 2–3 drops of the
solution, the methylolation process formed. As the solution’s temperature decreased to
60
◦
C, the remaining component of urea from each of the three molar ratios of F to MU
(equivalent to 6.9 g) was also added while the mixer was mixing the solution in all stages.
As the heater was turned off and was removed from the oil bath, the solution was put
aside to cool down. Finally, adding 2–3 drops of ammonium chloride 20%, the solution’s
pH became neutral to be more durable. Hence, three types of MUF resin were made with
different F to MU molar ratios.
J. Compos. Sci. 2023,7, 206 5 of 20
Table 1. Design of experiment (actual and coded values of the input factors).
№x1x2x3MR WR Tem №x1x2x3MR WR Tem
1 1 1 1 1.93 60 180 18 1 −1 1 1.93 20 180
2 1 1 −1 1.93 60 140 19 −1 0 0 1.68 40 160
3 0 0 0 1.805 40 160 20 −1−1 1 1.68 20 180
4 0 0 0 1.805 40 160 21 −1 1 1 1.68 60 180
5 0 −1 0 1.805 20 160 22 −1−1 1 1.68 20 180
6 0 0 1 1.805 40 180 23 1 1 −1 1.93 60 140
7 1 −1−1 1.93 20 140 24 −1 1 −1 1.68 60 140
8 0 1 0 1.805 60 160 25 0 0 0 1.805 40 160
9−1−1−1 1.68 20 140 26 0 −1 0 1.805 20 160
10 −1 1 1 1.68 60 180 27 1 0 0 1.93 40 160
11 0 0 1 1.805 40 180 28 1 −1−1 1.93 20 140
12 0 0 0 1.805 40 160 29 1 0 0 1.93 40 160
13 0 0 −1 1.805 40 140 30 0 1 0 1.805 60 160
14 0 0 −1 1.805 40 140 31 1 1 1 1.93 60 180
15 1 −1 1 1.93 20 180 32 −1 1 −1 1.68 60 140
16 0 0 0 1.805 40 160 33 −1 0 0 1.68 40 160
17 −1−1−1 1.68 20 140 34 0 0 0 1.805 40 160
Based on the design of experiment (DOE), the MUF-MP adhesive was obtained with
MUF to MP weight ratios equal to 20:80, 40:60 and 60:40 after mixing and putting it in a
mechanical stirrer for 15 min with the number of rotations 2000 rpm, and a homogeneous
solution was obtained. After the uniform distribution of the adhesive on the upper and
lower surfaces of the middle layer and the establishment of the upper and lower layers, the
assembly was put into the press under a pressure equal to 30 kg/cm
2
for 20 min at a certain
temperature. Then, after removing the wood laminated panel from the press, it was air-
conditioned for 2 weeks. Then, the panels were cut with the dimensions
350 ×20 ×20 mm
and three-point bending test was performed according to the EN 302 Standard with the
loading perpendicular to the glue line at the speed 5 mm/s. Equation (2) was used to
determine the bending strength of the panel. The results obtained from the bending test of
the samples were used to develop a modeling by the ANN approach to predict the bending
behavior of panel.
MOR =3Pmax ×L
2bS2(2)
where Pmax is maximum load (N), Lis span of panel (mm), bis width of panel, and Sis the
panel thickness.
After performing the bending test, the experimental values of the MOR obtained
were compared with the results predicted by applying the ANN approach to evaluate
the performance of the model developed and determine the effects of the variables, their
interactive effects and the optimum level of the application of each variable to produce
wood laminated panels, subsequently.
2.2.4. Artificial Neural Network (ANN) Analysis
ANN was used both for predicting the bending strength and determining the optimal
manufacturing parameters offering the high bending strength of panel. The F to MU molar
ratio (MR), the weight ratio of the modified protein (MP) to MUF resin (WR) and the press
temperature (Tem) have been the independent variables as inputs in the ANN modeling.
The data obtained from the experimental studies were modeled using MATLAB Neural
Network Toolbox. The experimental data were categorized into three training, testing and
validation data sets to determine the effect of the making process parameters on panel’s
MOR. The training data set was used to develop the network and the testing data set was
used to evaluate the model’s performance while the validation data set was used to validate
the developed model. To model the MOR, 5 data (15% of all data) were considered for the
testing set, 5 data (15% of all data) were considered for the validation set and 24 data (70%
J. Compos. Sci. 2023,7, 206 6 of 20
of all data) were considered for the training set. The data sets used for the model prediction,
the results of the ANN analysis and the experimental results are given in Table 2.
Table 2. The experimental and estimated results of the bending strength test of panels.
№x1x2x3Actual
Value
Predicted
Value №x1x2x3Actual
Value
Predicted
Value
1 1 1 1 124.40 125.57 18 1 −1 1 109.33 111.30
211−1 106.14 98.34 19 −1 0 0 127.49 120.10
3 0 0 0 103.36 114.67 20 −1−1 1 116.73 114.44
4 0 0 0 106.91 114.67 21 −1 1 1 142.92 143.15
5 0 −1 0 101.42 99.36 22 −1−1 1 112.54 114.44
6 0 0 1 125.28 127.86 23 1 1 −1 98.32 98.34
7 1 −1−1 77.95 77.21 24 −1 1 −1 110.48 102.05
8 0 1 0 124.38 126.11 25 0 0 0 122.5 114.67
9−1−1−1 66.49 67.81 26 0 −1 0 97.76 99.36
10 −1 1 1 142.06 143.15 27 1 0 0 114.32 118.81
11 0 0 1 132.47 127.85 28 1 −1−1 76.49 77.21
12 0 0 0 121.50 114.67 29 1 0 0 123.3 118.81
13 0 0 −1 82.57 82.64 30 0 1 0 127.85 126.11
14 0 0 −1 86.80 82.64 31 1 1 1 125.49 125.57
15 1 −1 1 111.37 111.30 32 −1 1 −1 114.83 102.05
16 0 0 0 120.50 114.67 33 −1 0 0 120.16 120.10
17 −1−1−1 69.05 67.814 34 0 0 0 120.9 114.67
Following the testing process, the actual (measured) values were compared with
the predicted values obtained from the ANN analysis. The models offered the best
prediction values by inserting them in the calculations of the R
2
(Equation (3)), RMSE
(Equation (4)), MAE (Equation (5)) and SSE (Equation (6)) that are well-known useful
performance functions.
R2="n
∑
i=1xi−
−
xyi−
−
y/sn
∑
i=1xi−
−
x2n
∑
i=1yi−
−
y2#2
(3)
RMSE =s1
n
n
∑
i=1
(xi−yi)2(4)
MAE =
∑xi−
−
x
n(5)
SSE =
n
∑
i=1xi−
−
x2
(6)
where nis the number of observations, x
i
and
−
x
are the observed and their average values,
yiand ¯
y are the related predicted and average values, respectively.
As R
2
increases and approaches 1, the predicted values approach the experimental val-
ues, showing the high ability of the model to predict the response with a high precision. As
the errors become minimum, the model being examined also offers the response prediction
more precisely.
The structure of the prediction model network including one input layer, one hidden
layer and one output layer. In the ANN structure, the F to MU molar ratio (MR), the weight
ratio of the modified protein to the MUF resin (WR) and the press temperature (Tem) were
chosen as the input variables while the MOR was chosen as the output variable. The
number of the process elements (neurons) of the hidden layer has been 6 for the model.
J. Compos. Sci. 2023,7, 206 7 of 20
To determine the MOR prediction model, the feed forward backpropagation multi-
layer ANNs were used. In the model offered, the hyperbolic tangent sigmoid function
(tansig) was preferred as the transfer function in the hidden layer while the linear transfer
function (purelin) was used in the output layer. After testing the errors obtained using
Levenberg-Marquardt backpropagation (trainlm), scaled conjugate gradient (trainscg) and
Bayesian regularization backpropagation (trainbr) algorithms as the training algorithms and
determining the suitable algorithm with the least error, the momentum gradient reduction
backpropagation algorithm (trainlm) was used to train the rules, and the mean square error
(MSE) calculated by the Equation (7) was preferred as the performance function.
MSE =1
N
N
∑
i=1
(ti−tdi)2(7)
where ti is the actual output (target values), td
i
is the neural network output (estimated
values) and Nis the number of training patterns.
To involve the models for each parameter uniformly, data of the training, testing and
validation data sets were normalized from
−
1 to +1 when the hyperbolic tangent sigmoid
function was used in the models and then, data were de-normalized to their original
values so that the results were interpretable. The normalization operation was possible by
applying the Equation (8):
Xnor =2×X−Xmin
Xmax −Xmin
−1 (8)
where X
nor
is the normalized value of the variable X(the actual value of the variable) and
Xmax and Xmin are the maximum and minimum values of Xrespectively.
2.3. Characterization Analysis
To examine the changes in the surface functional chemical groups, the Fourier trans-
form infrared (FT-IR) spectroscopy analysis was performed using the pelletized samples.
About 100 mg of potassium bromide (KBr) was mixed with 2 mg of the ground sample of
the cured index adhesives. The prepared samples were scanned using the Thermo Scientific
Nicolet 6700 FT-IR Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) in the
wavelength range from 600 to 4000 cm−1.
The thermogravimetry analysis was performed to receive the thermal behavior of the
index adhesives. For this purpose, Shimadzu TGA-50 apparatus (Shimadzu Corporation,
Kyoto, Japan) was used applying 10 mg of the powdered sample. The samples were
heated from 30 to 400
◦
C with the heating speed 10
◦
C/min with the nitrogen flow fixed at
20 mL/min to create a neutral environment.
3. Results and Discussion
The model was trained, validated and tested using 34 data (Table 2). Figure 2shows the
error changes of the chosen neural network graphically. It is observed that the performance
function reached its minimum value (16.2269) at the end of the second iteration (epochs)
for the MOR.
The regression analysis is used often between the predicted and measured values
to validate the networks. The accuracy of the models’ estimation increases when the
coefficients of correlation or determination go to one. It shows that there is a perfect fit
between the actual and predicted values here. Figure 3shows the correlation between the
calculated and actual values (training R
2
= 0.964, testing R
2
= 0.9111). R
2
in the testing set
shows that the network explains at least 91.11% of the actual data. These values prove
that the developed models have good performance and support the application of the
ANNs predictors. The normality hypothesis evaluates through the x-y normal plot that
the defined points must follow a linear form if the data values are the result of a normal
distribution. x-y plot indicates that the residuals are distributed almost normally. However,
J. Compos. Sci. 2023,7, 206 8 of 20
the plots did not deviate from the expected identity line. These results confirm that the
data support the ANN hypotheses.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 8 of 21
the cured index adhesives. The prepared samples were scanned using the Thermo Scien-
tic Nicolet 6700 FT-IR Spectrometer (Thermo Fisher Scientic, Waltham, MA, USA) in
the wavelength range from 600 to 4000 cm−1.
The thermogravimetry analysis was performed to receive the thermal behavior of the
index adhesives. For this purpose, Shimadzu TGA-50 apparatus (Shimadzu Corporation,
Kyoto, Japan) was used applying 10 mg of the powdered sample. The samples were
heated from 30 to 400 °C with the heating speed 10 °C/min with the nitrogen ow xed at
20 mL/min to create a neutral environment.
3. Results and Discussion
The model was trained, validated and tested using 34 data (Table 2). Figure 2 shows
the error changes of the chosen neural network graphically. It is observed that the perfor-
mance function reached its minimum value (16.2269) at the end of the second iteration
(epochs) for the MOR.
Figure 2. Changes in the MSE at each iteration for the MOR estimation models.
The regression analysis is used often between the predicted and measured values to
validate the networks. The accuracy of the models’ estimation increases when the coe-
cients of correlation or determination go to one. It shows that there is a perfect t between
the actual and predicted values here. Figure 3 shows the correlation between the calcu-
lated and actual values (training R2 = 0.964, testing R2= 0.9111). R2 in the testing set shows
that the network explains at least 91.11% of the actual data. These values prove that the
developed models have good performance and support the application of the ANNs pre-
dictors. The normality hypothesis evaluates through the x-y normal plot that the dened
points must follow a linear form if the data values are the result of a normal distribution.
x-y plot indicates that the residuals are distributed almost normally. However, the plots
did not deviate from the expected identity line. These results conrm that the data support
the ANN hypotheses.
Figure 2. Changes in the MSE at each iteration for the MOR estimation models.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 9 of 21
.
Figure 3. The comparison of the estimated and actual values (a) training data set, (b) testing data
set.
As it was stated before, RMSE, MAE and SSE were employed to evaluate the model’s
performance. The RMSE, MAE and SSE results (Table 3) indicate that the trainlm algo-
rithm has the least error in the training, testing and validation data sets and also in all data
sets with the RMSE values including 3.944, 7.218, 15.305 and 4.962, MAE values including
2.663, 6.184, 5.563 and 2.607 and SSE values including 375, 260, 201 and 837 compared to
the trainscg and trainbr based on the RMSE, MAE and SSE statistics. Hence, the optimum
network structure was obtained with the minimum RMSE, MAE and SSE values to esti-
mate the MOR in a network with 6 neurons in the hidden layer (1-6-3). The low RMSE
value is a parameter that indicates whether the performance of the chosen model is suita-
ble or not [39]. It is observed in Table 3 that it is beer to evaluate the model based on the
testing set due to more data points in the training set that can lead to more likely high
non-normal values [40]. In sum, it was determined that the whole performance of the
model decreased as the number of neurons increased beyond a set limit. Similar results
were also obtained by Schaop et al. [41] that stated that ANN is more practical and reliable
if the number of neurons is set in terms of the number of inputs.
Table 3. The comparison of the ANN models generated for the MOR estimation.
Source
TrainLM
TrainSCG
TrainBR
Tra.
Tes.
Val.
All
Tra.
Tes.
Val.
All
Tra.
Tes.
Val.
All
RMSE
3.954
7.218
15.305
4.962
10.138
13.759
19.996
19.996
4.857
8.369
28.056
5.345
MAE
2.663
6.184
5.563
2.607
8.228
13.267
7.336
8.838
3.863
7.625
2.473
4.211
SSE
375
260
201
837
2466
946
440
3853
566
350
55
971
The residuals of the model were tested to check the variance linearity and homoge-
neity hypotheses. In Figure 4, the standardized residuals are scaered somewhat similarly
above and below their zero middle, showing that the data have supported the ANN hy-
potheses. It is observed that more than 70% of the errors in the range below 5% and −3%
are in the ANN model developed based on the trainlm algorithm.
(b) Y test = 0.9326x + 2.2978
R² = 0.9111
(a) Y training = 0.9682x + 3.477
R² = 0.964
80
90
100
110
120
130
140
150
80 100 120 140 160
Estimated MOR (MPa)
Actual MOR (MPa)
Figure 3.
The comparison of the estimated and actual values (a) training data set, (b) testing data set.
As it was stated before, RMSE, MAE and SSE were employed to evaluate the model’s
performance. The RMSE, MAE and SSE results (Table 3) indicate that the trainlm algorithm
has the least error in the training, testing and validation data sets and also in all data sets
with the RMSE values including 3.944, 7.218, 15.305 and 4.962, MAE values including 2.663,
6.184, 5.563 and 2.607 and SSE values including 375, 260, 201 and 837 compared to the
trainscg and trainbr based on the RMSE, MAE and SSE statistics. Hence, the optimum
network structure was obtained with the minimum RMSE, MAE and SSE values to estimate
the MOR in a network with 6 neurons in the hidden layer (1-6-3). The low RMSE value
is a parameter that indicates whether the performance of the chosen model is suitable or
not [
39
]. It is observed in Table 3that it is better to evaluate the model based on the testing
set due to more data points in the training set that can lead to more likely high non-normal
values [
40
]. In sum, it was determined that the whole performance of the model decreased
as the number of neurons increased beyond a set limit. Similar results were also obtained
by Schaop et al. [
41
] that stated that ANN is more practical and reliable if the number of
neurons is set in terms of the number of inputs.
J. Compos. Sci. 2023,7, 206 9 of 20
Table 3. The comparison of the ANN models generated for the MOR estimation.
Source TrainLM TrainSCG TrainBR
Tra. Tes. Val. All Tra. Tes. Val. All Tra. Tes. Val. All
RMSE 3.954 7.218 15.305 4.962 10.138 13.759 19.996 19.996 4.857 8.369 28.056 5.345
MAE 2.663 6.184 5.563 2.607 8.228 13.267 7.336 8.838 3.863 7.625 2.473 4.211
SSE 375 260 201 837 2466 946 440 3853 566 350 55 971
The residuals of the model were tested to check the variance linearity and homogeneity
hypotheses. In Figure 4, the standardized residuals are scattered somewhat similarly above
and below their zero middle, showing that the data have supported the ANN hypotheses.
It is observed that more than 70% of the errors in the range below 5% and
−
3% are in the
ANN model developed based on the trainlm algorithm.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 10 of 21
Figure 4. The residual error values for all data set.
The plots of the comparison between the actual and predicted values are given in
Figure 5. The comparison of the actual and predicted values for the data sets not only
showed the predictability of ANN to know the MOR data but it also proved the generali-
zability of the model for indenite unknown MOR data. As it is observed, the values are
very close to each other and there is a t almost in all testing, training and validation data
sets. It increases the applicability of the ANN model.
Figure 5. The comparison of the measured and predicted MOR values with the best-t ANN model
for the testing (black and red markers), training and validation (blue markers) data sets (triangle
and star markers are actual and estimated values, respectively).
Researchers have used the experimental data to study the eect of F to U molar ratios,
bio-based adhesive application, press time and temperature, etc on the bending strength
through ANOVA or the regression tree method directly. Although ANOVA and the re-
gression tree method based on mathematical analysis could provide more detailed infor-
mation compared to 2D or 3D charts, the size of the data resulting from experiment could
be limited normally. The precision of the analysis results must be aected based on several
limited data. A reliable ANN bending strength prediction model can describe the mathe-
matical relation between dierent parameters of making a composite and MOR manually.
However, the ANN model was a black box through which the visible eects of the com-
posite making parameters on the MOR could be oered hardly. Using the ANN, it is pos-
sible to produce enough data to analyze the making parameters aecting the MOR
through 2D and 3D charts. For instance, if one is interested in the eect of WR on MOR,
data can be produced that are given in Table 4. Then, the curve analysis can be obtained
-20
-10
0
10
20
Residual error
60
70
80
90
100
110
120
130
140
150
1 2 3 4 5 6 7 8 9 101112 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2930
MOR (MPa)
Expemiment №
Figure 4. The residual error values for all data set.
The plots of the comparison between the actual and predicted values are given in
Figure 5
. The comparison of the actual and predicted values for the data sets not only
showed the predictability of ANN to know the MOR data but it also proved the generaliz-
ability of the model for indefinite unknown MOR data. As it is observed, the values are
very close to each other and there is a fit almost in all testing, training and validation data
sets. It increases the applicability of the ANN model.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 10 of 21
Figure 4. The residual error values for all data set.
The plots of the comparison between the actual and predicted values are given in
Figure 5. The comparison of the actual and predicted values for the data sets not only
showed the predictability of ANN to know the MOR data but it also proved the generali-
zability of the model for indenite unknown MOR data. As it is observed, the values are
very close to each other and there is a t almost in all testing, training and validation data
sets. It increases the applicability of the ANN model.
Figure 5. The comparison of the measured and predicted MOR values with the best-t ANN model
for the testing (black and red markers), training and validation (blue markers) data sets (triangle
and star markers are actual and estimated values, respectively).
Researchers have used the experimental data to study the eect of F to U molar ratios,
bio-based adhesive application, press time and temperature, etc on the bending strength
through ANOVA or the regression tree method directly. Although ANOVA and the re-
gression tree method based on mathematical analysis could provide more detailed infor-
mation compared to 2D or 3D charts, the size of the data resulting from experiment could
be limited normally. The precision of the analysis results must be aected based on several
limited data. A reliable ANN bending strength prediction model can describe the mathe-
matical relation between dierent parameters of making a composite and MOR manually.
However, the ANN model was a black box through which the visible eects of the com-
posite making parameters on the MOR could be oered hardly. Using the ANN, it is pos-
sible to produce enough data to analyze the making parameters aecting the MOR
through 2D and 3D charts. For instance, if one is interested in the eect of WR on MOR,
data can be produced that are given in Table 4. Then, the curve analysis can be obtained
-20
-10
0
10
20
Residual error
60
70
80
90
100
110
120
130
140
150
1 2 3 4 5 6 7 8 9 101112 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2930
MOR (MPa)
Expemiment №
Figure 5.
The comparison of the measured and predicted MOR values with the best-fit ANN model
for the testing (black and red markers), training and validation (blue markers) data sets (triangle and
star markers are actual and estimated values, respectively).
J. Compos. Sci. 2023,7, 206 10 of 20
Researchers have used the experimental data to study the effect of F to U molar
ratios, bio-based adhesive application, press time and temperature, etc on the bending
strength through ANOVA or the regression tree method directly. Although ANOVA and
the regression tree method based on mathematical analysis could provide more detailed
information compared to 2D or 3D charts, the size of the data resulting from experiment
could be limited normally. The precision of the analysis results must be affected based
on several limited data. A reliable ANN bending strength prediction model can describe
the mathematical relation between different parameters of making a composite and MOR
manually. However, the ANN model was a black box through which the visible effects of
the composite making parameters on the MOR could be offered hardly. Using the ANN, it
is possible to produce enough data to analyze the making parameters affecting the MOR
through 2D and 3D charts. For instance, if one is interested in the effect of WR on MOR,
data can be produced that are given in Table 4. Then, the curve analysis can be obtained
like what is presented in Figure 6. If one is interested in studying the interactive effects of
MR
×
WR, MR
×
Tem or WR
×
Tem on the MOR, data can be produced like what is given
in Table 5. Then, the curve analysis can be obtained like what is offered in Figures 7and 8.
Table 4. Example data for analyzing a single parameter’s influence on MOR.
No. MR WR Tem (◦C) MOR (MPa)
1 1.68 20 160 91.203
2 1.68 25 160 99.358
3 1.68 30 160 107.51
. . . . .
. . . . .
. . . . .
8 1.68 55 160 126.64
9 1.68 60 160 127.57
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 11 of 21
like what is presented in Figure 6. If one is interested in studying the interactive eects of
MR × WR, MR × Tem or WR × Tem on the MOR, data can be produced like what is given
in Table 5. Then, the curve analysis can be obtained like what is oered in Figures 7 and
8.
Table 4. Example data for analyzing a single parameter’s inuence on MOR.
No.
MR
WR
Tem (°C)
MOR (MPa)
1
1.68
20
160
91.203
2
1.68
25
160
99.358
3
1.68
30
160
107.51
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
8
1.68
55
160
126.64
9
1.68
60
160
127.57
Table 5. Example data for analyzing multiple parameters’ inuence on MOR.
No.
MR
WR
Tem (°C)
MOR (MPa)
1
1.68
20
140
91.203
2
1.71125
25
140
101.46
3
1.7425
30
140
111.71
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
82
1.68
20
140
90.12
83
1.71125
20
145
97.234
84
1.7425
20
150
104.26
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
243
1.93
60
180
134.36
Figure 6. The direct eect of parameters on MOR and comparison of them with the best-t ANN
model.
85
90
95
100
105
110
115
120
125
1.68 1.805 1.93 20 40 60 140 160 180
MR WR Tem
Targets Outputs
MOR (MPa)
Figure 6.
The direct effect of parameters on MOR and comparison of them with the best-fit
ANN model.
J. Compos. Sci. 2023,7, 206 11 of 20
Table 5. Example data for analyzing multiple parameters’ influence on MOR.
No. MR WR Tem (◦C) MOR (MPa)
1 1.68 20 140 91.203
2 1.71125 25 140 101.46
3 1.7425 30 140 111.71
. . . . .
. . . . .
. . . . .
82 1.68 20 140 90.12
83 1.71125 20 145 97.234
84 1.7425 20 150 104.26
. . . . .
. . . . .
. . . . .
243 1.93 60 180 134.36
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 12 of 21
Figure 7. The interaction eect of parameters on MOR and comparison of them with the best-t
ANN model.
Figure 8. Counter plot of interaction eect of MR × WR (a), MR × Tem (b), and WR × Tem (c) on
MOR estimated by ANN.
Similarly, the curves showing the direct eect of MR, WR and Tem on MOR are given
in Figure 6 and the interactive eect of the independent variables on the MOR is given in
Figures 7 and 8. It is evident in Figure 6 that the average MOR increases slightly as the
MR increases and it decreases sharply afterwards. The changes in the actual MOR are
largely similar to those of the estimated MOR so that there is a perfect match at the maxi-
mum MR. As the protein content increases to 40%, the increase in MOR is linear, and the
intensity of its increase decreases afterwards and becomes nonlinear. The maximum MOR
is where the MUF resin is replaced by the maximum protein. The changes in the actual
MOR agree largely with the estimated value while the error between both values increases
slightly as maximum protein is used. As the press temperature increases to the middle
level (160 °C), the actual and estimated MOR increase linearly while as the press temper-
ature increases to 180 °C, the changes in the increase of MOR increase nonlinearly (125
MPa). There is a perfect match between the actual and estimated values of MOR in the
eect of the changes in the press temperature.
It is observed in Figure 7 that there is a perfect match between the estimated values
of MOR resulting from the eect of MR × WR (x1x2), MR × Tem (x1x3) and WR × Tem
70.5
80.5
90.5
100.5
110.5
120.5
130.5
140.5
20 40 60 140 160 180 140 160 180
MR×WR MR×Tem WR×Tem
Estimated MR 1.68:1 Actual
Estimated MR 1.805:1 Estimated MR 1.93:1
Estimated WR 20% Estimated WR 40%
Estimated WR 60%
MOR (MPa)
Figure 7.
The interaction effect of parameters on MOR and comparison of them with the best-fit
ANN model.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 12 of 21
Figure 7. The interaction eect of parameters on MOR and comparison of them with the best-t
ANN model.
Figure 8. Counter plot of interaction eect of MR × WR (a), MR × Tem (b), and WR × Tem (c) on
MOR estimated by ANN.
Similarly, the curves showing the direct eect of MR, WR and Tem on MOR are given
in Figure 6 and the interactive eect of the independent variables on the MOR is given in
Figures 7 and 8. It is evident in Figure 6 that the average MOR increases slightly as the
MR increases and it decreases sharply afterwards. The changes in the actual MOR are
largely similar to those of the estimated MOR so that there is a perfect match at the maxi-
mum MR. As the protein content increases to 40%, the increase in MOR is linear, and the
intensity of its increase decreases afterwards and becomes nonlinear. The maximum MOR
is where the MUF resin is replaced by the maximum protein. The changes in the actual
MOR agree largely with the estimated value while the error between both values increases
slightly as maximum protein is used. As the press temperature increases to the middle
level (160 °C), the actual and estimated MOR increase linearly while as the press temper-
ature increases to 180 °C, the changes in the increase of MOR increase nonlinearly (125
MPa). There is a perfect match between the actual and estimated values of MOR in the
eect of the changes in the press temperature.
It is observed in Figure 7 that there is a perfect match between the estimated values
of MOR resulting from the eect of MR × WR (x1x2), MR × Tem (x1x3) and WR × Tem
70.5
80.5
90.5
100.5
110.5
120.5
130.5
140.5
20 40 60 140 160 180 140 160 180
MR×WR MR×Tem WR×Tem
Estimated MR 1.68:1 Actual
Estimated MR 1.805:1 Estimated MR 1.93:1
Estimated WR 20% Estimated WR 40%
Estimated WR 60%
MOR (MPa)
Figure 8.
Counter plot of interaction effect of MR
×
WR (
a
), MR
×
Tem (
b
), and WR
×
Tem (
c
) on
MOR estimated by ANN.
J. Compos. Sci. 2023,7, 206 12 of 20
Similarly, the curves showing the direct effect of MR, WR and Tem on MOR are given
in Figure 6and the interactive effect of the independent variables on the MOR is given in
Figures 7and 8. It is evident in Figure 6that the average MOR increases slightly as the MR
increases and it decreases sharply afterwards. The changes in the actual MOR are largely
similar to those of the estimated MOR so that there is a perfect match at the maximum MR.
As the protein content increases to 40%, the increase in MOR is linear, and the intensity of
its increase decreases afterwards and becomes nonlinear. The maximum MOR is where
the MUF resin is replaced by the maximum protein. The changes in the actual MOR agree
largely with the estimated value while the error between both values increases slightly as
maximum protein is used. As the press temperature increases to the middle level (160
◦
C),
the actual and estimated MOR increase linearly while as the press temperature increases
to 180
◦
C, the changes in the increase of MOR increase nonlinearly (125 MPa). There is a
perfect match between the actual and estimated values of MOR in the effect of the changes
in the press temperature.
It is observed in Figure 7that there is a perfect match between the estimated values of
MOR resulting from the effect of MR
×
WR (x1x2), MR
×
Tem (x1x3) and
WR ×Tem
(x2x3)
when the third factor of each group of the interactive effects is at the middle level. Different
statistics being studied such as R2, RMSE, MAE and SSE also confirm the agreement or
in other words, the minimum error between the estimated and actual values (Table 3).
According to the nonlinear regression model resulting from the MOR prediction (Equa-
tion (9), y ~ f(b,x)), the regression coefficients of the direct, interactive and square effects
of the variables together with the related mathematical signs (
−
or +) indicate that MOR
is affected differently for each source of change when the sources have been significant
statistically. It is observed that according to the significance of each source of change, the
direct effect of the change in the press temperature (19.427) results in the maximum change
in the response being examined followed by the direct effect of the protein content (12.51).
However, the square effect of Tem (
−
10.648) has the maximum effect on the MOR decrease
followed by the interactive effect of MR ×WR.
y ~ f(b,x) = 115.37 −1.632x1 + 12.51x2 + 19.427x3 + 3.5542x12−3.1611x22−
10.648x32−3.4452x1x2 −3.3005x1x3 −1.5461x2x3 (9)
The trained ANN can provide the intermediate values required for optimizing the
production process. In other words, thanks to the well-trained model, the outputs can
be detected and tracked according to certain input values with a high precision without
doing more experimental studies [
42
]. All outputs of the effects of the parameters on the
dependent variable can be predicted by ANN for various combinations. The MOR values
estimated by the ANN prediction model are given in Figures 7and 8according to different
levels of MR, the WR and Tem. When the Tem is fixed at the middle level (160
◦
C), the
prediction of the interactive effect of MR and WR on MOR indicated that as WR increases
for each MR (ranging from minimum (1.68) to maximum (1.93)), MOR becomes maximum.
As WR becomes minimum continuously, MOR approaches the minimum value and the
minimum value is where MR is at the middle level (1.805) (Figure 8a). Also, in other
trial, while WR was fixed at the middle level (40:60) and MR and the Tem changed, the
results indicated that as the Tem increases continuously from 140 to 180
◦
C for all ranges of
MR from 1.68 to 1.93, MOR becomes maximum. However, the minimum MOR decreases
continuously as the Tem decreases and its minimum value is where MR is at the middle
level (1.805) (Figure 8b). The relationship between MOR and the interactive effect of WR
and Tem is shown in Figure 8c. It is observed that the interactive effects of both parameters
are less than the interactive effects of other parameters on MOR. While MR is at the
middle level (1.805), as WR increases to maximum and Tem becomes maximum, MOR also
becomes maximum. However, as MR decreases to minimum and Tem becomes minimum
simultaneously, MOR becomes minimum. By increasing the molar ratio of formaldehyde to
melamine urea, more di hydroxymethylureas and trihydroxymethylureas are produced in
the MUF resin and it causes the molecular weight to be higher with longer chains resulting
J. Compos. Sci. 2023,7, 206 13 of 20
from methylene and methylene ether bonds and more branched chains [
43
]. On the other
hand, by using NH
4
Cl, the coagulation speed and temperature can be reduced as a result
of the release of hydrochloric acid, so that the higher the molar ratio of formaldehyde, the
higher the coagulation speed of MUF [
44
]. But due to the non-use of NH
4
Cl, which was due
to the possibility of determining the effect of hydrolyzed protein on the coagulation speed
of MUF resin, increasing the temperature has completed the polycondensation reaction.
The viscosity of UF molecules is constant for all ranges of the shear rate due to
their small size showing Newtonian behavior. However, protein or protein compound
shows a non-Newtonian behavior even for its small amount mixed with UF resin and
can align with the shear flow in contrast to the UF resin [
45
]. As a result, producing the
shear thinning behavior, the modified protein can act as a rheological modifier in the UF
resin. The result of this behavior is the increase in the apparent viscosity for low amounts
of protein addition due to the interactive effects of protein-protein and protein-water
through hydrogen bonds [
46
]. However, as the protein content increases more, viscosity
decreases at high shear ranges (
≈
103 s
−1
) until it reaches the amounts similar to the UF
amounts and a rheological behavior is generated similar to when the adhesive (UF resin) is
applied [
47
]. In this process, even as protein increases slightly, an entangled structure with a
pseudoplastic behavior forms [
47
], showing a gel-like behavior. The result of this behavior
is the increase in the suspension structure strength and the glue line building stability in
which if protein is treated by NaOH, the occurred denaturation increases the polymer’s
random coil volume [
48
] so that more entanglement occurs in the adhesive emulsion
containing more modified protein. Since the UF resins have three distinct variation stages
as a function of the shear rate including shear thinning—Newtonian—shear thinning
behaviors respectively at the beginning, middle and end of a viscosity-shear rate curve
while the combination of the UF and modified protein only contains two regions of shear-
thinning and approximate Newtonian behaviors respectively at the beginning and end of
such a curve [
6
], protein molecules can be considered as an effective rheological modifier
for MUF. In this way, protein plays the role of a filler or additive. Meanwhile, according to
one of the main methods of adhesive application (roller coating), as protein increases, the
adhesive experiences a deeper shear thinning behavior so that it can be an advantage for the
roller coating method. However, due to the lack of proper activation of spherical particles
rich in unmodified protein, the possibility of proper connection or good support with UF
resin molecules and as a result its deposition in the suspension is not provided, so that three
distinct points of shear thickening—shear thinning—approximate Newtonian behaviors
appear in the viscosity-shear rate curve again [
6
]. Hence, according to the protein activation
level and the intensity of its treatment or the increase in its amount in the adhesive, the
bonding strength will increase continuously.
Due to the presence of molecules with a high molecular weight, the addition of the
modified protein presents a complete pseudoplastic behavior so that the entanglement
between polymer chains will increase. The increase is apparently related to the effect of
the chemical crosslinkings inside the adhesive bulk and the chemical reactions between
the adhesive and substrate (wood) in the curing stage. The chemical cross-linking created
between the functional groups of carboxyl and uncoiled amine of peptides with active
sites of the MUF resin molecules in the curing stage leads to the formation of a hard
3D network with a high molecular weight of the polymer connected through covalence
linkages [
49
,
50
]. This phenomenon increases the cohesive strength inside the adhesive bulk
while the entanglements of the polymer chains increase simultaneously that protect them
from creeping during the mechanical bending test [
51
]. Hence, the chemical cross-linking
between the components of the adhesive system and the potential chemical interactive
effects between the adhesive and adherened in the curing step leads to the increase in
the strength, not only in the bending but also during the horizontal shear that can result
in delamination.
While a very high depth or very low depth of adhesive penetration leads to the
decrease in the bonding strength [
52
,
53
], the optimum penetration with a certain degree can
J. Compos. Sci. 2023,7, 206 14 of 20
increase the strength of the adhesives containing protein by developing 3D regions in the
interphase region. Adhesives with polymers with a very high molecular weight or viscosity
have a low depth penetration that result in ineffective mechanical interlocking and decrease
the bonding strength [
52
–
54
]. Properties such as viscosity and penetrability of the uncured
adhesive are affected by the intermolecular interactive effects and the extent of polymer
crosslinking [
55
]. Based on these proved facts, the press pattern at higher temperature can
develop the intermolecular interactive effects of peptides and resin molecules through two
factors: (1) high interactive effects between polymers lead to the increase in the molecular
weight and decrease in the penetrability of the cured adhesive and (2) high intermolecular
interactive effects lead to higher crosslinking in the curing step through which there is only
a lower number of active groups accessible to form connection with wood in the curing
step [
50
]. In these conditions, it was observed during the test that the failure mode is due
to a middle level rupture of wood and interphase rupture in practice. It means that in
effect using more protein along with a higher press temperature, the adhesion strength has
reached the cohesion strength presented by wood and adhesive.
In the bending test, it was observed practically that as the MUF to protein ratio
increased, horizontal failure occurred between the wood layers along the glue line (de-
lamination). However, as the protein weight ratio increased, failure occurred under pure
bending mode. Due to using no hardener such as NH
4
Cl that accelerates the self-curing
reaction of the MUF resin during the hot press and forms some CH
2
-O-CH
2
bridges that
are weaker than methylene bonds, chemical bonds can form that are composed of protein-
CH2-MUF bridges during the hot press. The necessity of using the MUF resin combined
with the soy adhesive to the extent 40% is reported [56].
With a structure containing hydrophilic regions covered by hydrophobic regions and
preventing the polar groups access, protein has a conformation including disulphide bonds,
non-covalent forces such as van der waals interactive effects, hydrogen bonds and elec-
trostatic interactive effects. During its treatment by NaOH, the tertiary and quaternary
structures are destroyed to some extent and the functional groups are exposed due to the
failure of the chemical bonds and intermolecular interactive effects of bonds and crosslink-
ing [
57
]. NH2 of the hydrolyzed protein reacts with the free formaldehyde resulting from
the absence of methylene connection that could occur between urea and formaldehyde
and meanwhile, the higher the content of the hydrolyzed protein or the intensity of pro-
tein hydrolysis is, the higher its reaction with formaldehyde due to the exposure of more
functional groups on protein will be.
Increasing protein and press temperature when F to M/U molar ratio is minimum,
the strength becomes maximum. It means that at the presence of more functional groups of
modified protein and higher press temperature, crosslinking occurs between these groups
and NH2. However, if formaldehyde molar ratio increases before protein reacts with NH2,
NH2 group of urea will have a methylation reaction with formaldehyde while the functional
group of protein has not reacted with NH2 and no curing has occurred yet [
57
]. In these
conditions, the modified protein can have a copolymerization reaction with urea/melamine
and formaldehyde and form a network structure. This increase can be due to the lower
remaining moisture along with the application of a higher range of temperature.
Characterization Analysis of Results
The FTIR spectrum of different adhesives is presented in Figure 9. The observed peak
at about 3340 cm
−1
is related to the free and connected OH groups and N-H bending
vibrations that can form hydrogen bonds with carbonyl group and peptide bonds in protein
and the wood surface. It is observed that as WR increased, the peak has become wider with
a lower intensity. However, as the press temperature increased, the peak’s intensity has
decreased again, showing the possibility of hydroxyl groups removal due to making new
connections with protein. The observed peak at about 2906 cm
−1
is due to the symmetric
and asymmetric stretching vibrations of methylene group in different adhesives. It is
observed that the peak’s intensity has increased in the adhesive containing more protein
J. Compos. Sci. 2023,7, 206 15 of 20
content. However, as the curing temperature increased, the peak’s intensity has decreased,
showing that using a higher temperature, the modified protein could take part in the
system’s reaction successfully.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 16 of 21
Figure 9. FTIR curves of adhesive containing dierent weight ratio of modied protein to MUF resin
with dierent ratio cured at minimum and maximum press temperature.
Due to the presence of abundant avonoid compounds in walnut wood, esterication
of tannin hydroxyls by protein chain acids has resulted in the peak of ester bond at 1731
cm−1. In this process, the reaction of protein with tannin avonoid units can occur either
through the amine group of protein’s lateral chain or esterication with the acid group of
protein’s lateral chain [58]. It is observed that the widening and decrease in the intensity
in this bond have occurred due to the increase in WR and Tem to some extent while MR
had no eect. This means that even during the use of lower amounts of formaldehyde to
melamine- urea, with the increase in protein consumption, probably the ester linkages
tend to be replaced by methylene linkages that give maximum resistance to glue line of
panel. Primary amide band (I type) in the range of 1630 to 1650 cm−1 is characteristic of
C=O stretching with a small amount of C-N stretching. As the modied protein increases
mainly containing amine groups, imine bonds formation will shift from 1646 to 1698 cm−1
oering the stretching bending imine (C=N). This bond shows the C=N formation in all
amine compounds where formaldehyde reacts with amines to produce methyl- and ethyl-
amine. It is conrmed by the increase in the peak’s intensity at the band 1646 cm−1 at the
same time with the weakening of the peak 1698 cm−1, showing that the adhesive has
formed more stable chemical bonds and has formed a denser structure. It means that ap-
plying more modied protein, more cross-linking has formed with the MUF resin. The
peak at 1558 cm−1 shows the presence of N-C=N bending and ring deformation vibration
in triazine ring [59]. As the protein content and press temperature increase, the obvious
collapse in the peak and its development to 1537 cm−1 indicate that the oxidized protein
has entered the network structure in melamine’s triazine ring eectively. Then, it may be
accompanied by the deformation and widening of the peak concentrated at 3340 cm−1 that
is due to the complete overlap of dierent N-H and OH environments [60]. The peak at
811 cm−1 also indicates triazine ring’s out-of-plane vibration. This peak along with the
peak at 1537 cm−1 are very important characteristics of melamine’s triazine ring [60]. The
spectra are completely consistent with the expected structure in the MUF-MP adhesives.
However, due to the decrease in the intensity of peaks at 811 cm−1 in the samples contain-
ing more protein at a higher press temperature, active placement of protein can be con-
sidered as a permanent part of the adhesive, showing eective uniform distribution of
protein in the MUF matrix. Free amine groups of protein are able to react with formalde-
hyde and accompany the MUF resin structure cured by the application of the press tem-
Figure 9.
FTIR curves of adhesive containing different weight ratio of modified protein to MUF resin
with different ratio cured at minimum and maximum press temperature.
Due to the presence of abundant flavonoid compounds in walnut wood, esterification
of tannin hydroxyls by protein chain acids has resulted in the peak of ester bond at
1731 cm
−1
. In this process, the reaction of protein with tannin flavonoid units can occur
either through the amine group of protein’s lateral chain or esterification with the acid
group of protein’s lateral chain [
58
]. It is observed that the widening and decrease in the
intensity in this bond have occurred due to the increase in WR and Tem to some extent while
MR had no effect. This means that even during the use of lower amounts of formaldehyde
to melamine- urea, with the increase in protein consumption, probably the ester linkages
tend to be replaced by methylene linkages that give maximum resistance to glue line of
panel. Primary amide band (I type) in the range of 1630 to 1650 cm
−1
is characteristic of
C=O stretching with a small amount of C-N stretching. As the modified protein increases
mainly containing amine groups, imine bonds formation will shift from 1646 to 1698 cm
−1
offering the stretching bending imine (C=N). This bond shows the C=N formation in all
amine compounds where formaldehyde reacts with amines to produce methyl- and ethyl-
amine. It is confirmed by the increase in the peak’s intensity at the band 1646 cm
−1
at
the same time with the weakening of the peak 1698 cm
−1
, showing that the adhesive has
formed more stable chemical bonds and has formed a denser structure. It means that
applying more modified protein, more cross-linking has formed with the MUF resin. The
peak at 1558 cm
−1
shows the presence of N-C=N bending and ring deformation vibration
in triazine ring [
59
]. As the protein content and press temperature increase, the obvious
collapse in the peak and its development to 1537 cm
−1
indicate that the oxidized protein
has entered the network structure in melamine’s triazine ring effectively. Then, it may be
accompanied by the deformation and widening of the peak concentrated at 3340 cm
−1
that
is due to the complete overlap of different N-H and OH environments [
60
]. The peak at
811 cm
−1
also indicates triazine ring’s out-of-plane vibration. This peak along with the peak
at 1537 cm
−1
are very important characteristics of melamine’s triazine ring [
60
]. The spectra
are completely consistent with the expected structure in the MUF-MP adhesives. However,
due to the decrease in the intensity of peaks at 811 cm
−1
in the samples containing more
protein at a higher press temperature, active placement of protein can be considered as
a permanent part of the adhesive, showing effective uniform distribution of protein in
J. Compos. Sci. 2023,7, 206 16 of 20
the MUF matrix. Free amine groups of protein are able to react with formaldehyde and
accompany the MUF resin structure cured by the application of the press temperature [
56
].
Based on the change in the intensity of the peak related to the C-O absorption of the
modified adhesive at 1027 cm
−1
showing that C-O is affected by protein, it is evident that
as the protein and press temperature increase, MUF is introduced to modified protein
successfully due to the rapid reaction of MUFP hydroxymethyl active groups [
61
] and a lot
of intermolecular hydrogen bonds are created with active reactive groups of the protein
molecule such as amine [
62
]. A suitable agreement was obtained between the results
obtained from FTIR analysis and the bending strength of the boards. It was found that
by increasing the use of modified protein and increasing the temperature of the press, it
was possible to create new connections between functional groups of protein with urea
and melamine. During the optimization of the production process of layered products, the
results indicated that the optimal value of bending strength is where the amount of protein
used and the press temperature were at the maximum possible level, while the molar ratio
of formaldehyde to melamine urea in it was moderate or low.
The thermogravimetric (TG) curve of the adhesive containing different MRs with
different WRs cured at different Tems of panels is given in Figure 10, showing three certain
stages of similar schematics of mass loss. These stages include water evaporation in the
range of temperature less than 160
◦
C, decomposition of small molecules resulting from
the modified protein broken by urea and melamine and also breakage of stable chemical
bonds at the temperature between 160–320
◦
C due to the breakage of intra- and inter-
molecular hydrogen bonds, electrostatic bonds and division of covalent bonding between
the remaining peptide bonds of amino acid [
63
] and destruction of S-S, O-N and O-O
bonds and finally, backbone peptide bonds of protein in the adhesive at the temperatures
above 320
◦
C producing gases such as CO, CO
2
, NH
3
and H
2
S [
64
]. As the protein content
increases in the compound, the weight-temperature slopes decrease. A similar trend is
observed when MR, WR and press temperature increase (orange and black lines) and also
when WR and press temperature are maximum but MR decreases (orange and red curves).
These stages of mass loss are also reflected in relation with the derivative thermogravimetric
(DTG) curve as shown in Figure 11. It is observed that as the press temperature increases
continuously, DTV curve peaks decrease in the second stage (from black to orange lines).
Simultaneously, as the modified protein added to the MUF increases, the intensity of the
DTV curve peaks decreases in the second stage (red and blue line). In addition, as MR
increases when the press temperature and the added protein content are maximum (blue
and orange lines), DTV curve peaks become minimum in the second stage, showing that as
the consumed protein becomes maximum, there is a maximum efficiency at the presence
of high press temperature and more formaldehyde molar ratio. However, the difference
between this treatment (orange line) and when minimum protein content and MR and
maximum temperature are applied (blue line) is minimum. It means that at the presence
of a lower protein content, application of a higher press temperature is more suitable.
Meanwhile, the process of urea and melamine modification by protein has resulted in the
formation of more stable chemical bonds and more smaller molecules, that have led to
descending changes in the intensity of the DTV curve peaks, showing the formation of
polysaccharide crosslinkings and dense network presenting a higher temperature stability.
The XRD results showed that there is a perfect match between the changes in peaks
resulting from increasing the amount of protein consumption and increasing the heat of
the press and the bending strength values of the manufactured panels. Also, based on the
change in the intensity of the peaks as a result of the change in the values of dependent
variables, by reducing the amount of protein consumption, higher levels of the molar ratio
of formaldehyde to MU can be used, while with the increase of protein consumption, the
use of lower levels of the molar ratio of formaldehyde to MU is essential. These results are
consistent with the optimized outputs by genetic algorithm coupled with ANN.
J. Compos. Sci. 2023,7, 206 17 of 20
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 18 of 21
Figure 10. TG curves of adhesives.
Figure 11. DTG curves of adhesives.
4. Conclusions
The present research has studied the ability of modeling the bending strength of the
laminated products made of walnut wood adhered by the bio-based hydrolyzed soybean
protein combined with MUF resin adhesive with dierent F to M/U molar ratios at dier-
ent press temperatures. The results showed that:
- The bending strength changes signicantly as the F to M/U molar ratio and the
weight ratio of the modied protein to MUF resin change so that as F to M/U molar
ratio decreases and the weight ratio of protein to MUF resin increases, the bending
strength increases. Also, in the interactive eect of MR and press temperature, as the
press temperature increases or decreases and the MR increases to a certain level and
as MR approaches the maximum value, MOR decreases. Furthermore, in the interac-
tive eect of the press temperature and weight ratio of protein to MUF resin, the in-
crease in the temperature and WR will result in the increase in the bending strength.
- The evaluation between the experimental values and those predicted by ANN re-
sulted in the presentation of an excellent relationship (with a dierence less than 5%)
for the estimated series of the process parameters.
- The ANN method could eectively produce experimental data resulting from the
determination of the bending strength of the laminated wood products so that using
suitable algorithms, ANN could oer a well-trained model to estimate the response
being examined through which the experimental costs and time could be saved to
determine the eect of each production variable on the response being examined.
- The diagnostic analysis presented by FTIR and TGA showed that urea, melamine and
free formaldehyde in resin could interact chemically with the modied soy protein
Figure 10. TG curves of adhesives.
J. Compos. Sci. 2023, 7, x FOR PEER REVIEW 18 of 21
Figure 10. TG curves of adhesives.
Figure 11. DTG curves of adhesives.
4. Conclusions
The present research has studied the ability of modeling the bending strength of the
laminated products made of walnut wood adhered by the bio-based hydrolyzed soybean
protein combined with MUF resin adhesive with dierent F to M/U molar ratios at dier-
ent press temperatures. The results showed that:
- The bending strength changes signicantly as the F to M/U molar ratio and the
weight ratio of the modied protein to MUF resin change so that as F to M/U molar
ratio decreases and the weight ratio of protein to MUF resin increases, the bending
strength increases. Also, in the interactive eect of MR and press temperature, as the
press temperature increases or decreases and the MR increases to a certain level and
as MR approaches the maximum value, MOR decreases. Furthermore, in the interac-
tive eect of the press temperature and weight ratio of protein to MUF resin, the in-
crease in the temperature and WR will result in the increase in the bending strength.
- The evaluation between the experimental values and those predicted by ANN re-
sulted in the presentation of an excellent relationship (with a dierence less than 5%)
for the estimated series of the process parameters.
- The ANN method could eectively produce experimental data resulting from the
determination of the bending strength of the laminated wood products so that using
suitable algorithms, ANN could oer a well-trained model to estimate the response
being examined through which the experimental costs and time could be saved to
determine the eect of each production variable on the response being examined.
- The diagnostic analysis presented by FTIR and TGA showed that urea, melamine and
free formaldehyde in resin could interact chemically with the modied soy protein
Figure 11. DTG curves of adhesives.
4. Conclusions
The present research has studied the ability of modeling the bending strength of the
laminated products made of walnut wood adhered by the bio-based hydrolyzed soybean
protein combined with MUF resin adhesive with different F to M/U molar ratios at different
press temperatures. The results showed that:
-
The bending strength changes significantly as the F to M/U molar ratio and the weight
ratio of the modified protein to MUF resin change so that as F to M/U molar ratio
decreases and the weight ratio of protein to MUF resin increases, the bending strength
increases. Also, in the interactive effect of MR and press temperature, as the press
temperature increases or decreases and the MR increases to a certain level and as
MR approaches the maximum value, MOR decreases. Furthermore, in the interactive
effect of the press temperature and weight ratio of protein to MUF resin, the increase
in the temperature and WR will result in the increase in the bending strength.
-
The evaluation between the experimental values and those predicted by ANN resulted
in the presentation of an excellent relationship (with a difference less than 5%) for the
estimated series of the process parameters.
-
The ANN method could effectively produce experimental data resulting from the
determination of the bending strength of the laminated wood products so that using
suitable algorithms, ANN could offer a well-trained model to estimate the response
being examined through which the experimental costs and time could be saved to
determine the effect of each production variable on the response being examined.
-
The diagnostic analysis presented by FTIR and TGA showed that urea, melamine and
free formaldehyde in resin could interact chemically with the modified soy protein and
J. Compos. Sci. 2023,7, 206 18 of 20
improve the bending strength of the laminated product so that as the modified protein
increased compared to the MUF resin, the chemical interactive effects intensified along
with the decrease in the F to M/U molar ratio.
Author Contributions:
Conceptualization, methodology, software, validation, formal analysis, inves-
tigation and resources, project administration, writing and original draft preparation, M.N. and F.N.;
investigation, visualization, writing—review and editing writing and supervision, A.N.P. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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