EVALUATION OF MECHANICAL AND FLAME RETARDANT PROPERTIES OF MEDIUM DENSITY FIBERBOARD USING ARTIFICIAL NEURAL NETWORK

The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatures. At first, the experimental design was created based on the Response Surface Methodology, and then the significance of each independent variable with respect to its effect on the responses was evaluated through ANOVA test. It was determined that the positive effects of increasing press temperatures on MOR compensated the negative effects of fire retardant content on it. However, ML decreases more at the same time. ANN results exhibited a good agreement with experimental results. It was shown that the prediction error was in an acceptable range. The results indicated that the developed ANN model can predict the MOR and ML of the fiberboard with an acceptable accuracy. Therefore, applying the proposed model can lead to obtain the desirable outputs of MOR and ML by performing fewer experiments, and spending less time and cost.

Keywords:
Artificial neural network; Strength; Mass loss; Press temperature; Fire retarding agente; Panel

Authorship

Morteza Nazerian ⁺ + morteza17172000@yahoo.com

Shahid Beheshti University, Tehran, IranShahid Beheshti UniversityIranTehran, IranShahid Beheshti University, Tehran, Iran

https://orcid.org/0000-0002-9319-2667

Sakine Shirzaii

University of Zabol, Zabol, IranUniversity of ZabolIranZabol, Iran University of Zabol, Zabol, Iran

Rahim Mohebbi Gargarii

University of Zabol, Zabol, IranUniversity of ZabolIranZabol, Iran University of Zabol, Zabol, Iran

https://orcid.org/0000-0001-5206-2252

Elham Vatankhah

Shahid Beheshti University, Tehran, IranShahid Beheshti UniversityIranTehran, IranShahid Beheshti University, Tehran, Iran

https://orcid.org/0000-0002-0626-8126

+ morteza17172000@yahoo.com

SCIMAGO INSTITUTIONS RANKINGS

Shahid Beheshti University, Tehran, IranShahid Beheshti UniversityIranTehran, IranShahid Beheshti University, Tehran, Iran

University of Zabol, Zabol, IranUniversity of ZabolIranZabol, Iran University of Zabol, Zabol, Iran

Figures | Tables | Formulas

Figures (9)
Tables (4)
Formulas (7)

Density (kg.m^-3)	Solid materials (%)	Viscosity (CP)	Gel time (s)	pH
1275	50	200-400	55	7.6

№	X1	X2	X3	X4	MOR (MPa)			Mass loss, %
					Tar.	Out.	Err.	Tar.	Out.	Err.
1	165	3.88	3.88	3.88	17	16.167	0.83	1.55	1.534	0.015
2	165	3.88	3.88	7.77	13	13.178	-0.17	0.6	0.454	0.145
3	180	1.94	5.83	1.94	17	17.216	-0.21	1.59	1.630	-0.040
4	195	3.88	3.88	3.88	21	21.051	-0.05	1.7	1.7358	-0.035
5	180	5.83	1.94	1.94	17	16.935	0.64	1.44	1.474	-0.034
6	150	5.83	5.83	1.94	12	11.623	0.37	0.85	0.866	-0.016
7	150	1.94	1.94	1.94	21	21.166	-0.16	1.65	1.567	0.0824
8	135	3.88	3.88	3.88	13	13.099	-0.09	1.38	1.3796	0.0003
9	150	5.83	5.83	5.83	8	9.245	-1.24	0.49	0.5099	-0.019
10	165	3.88	3.88	3.88	16	16.167	-0.16	1.56	1.534	0.0256
11	150	1.94	1.94	5.83	19	21.166	-2.16	1.49	1.567	-0.077
12	150	1.94	5.83	5.83	10	10.071	-0.07	1.22	1.2029	0.0170
13	165	3.88	0.00	3.88	19	19.157	-0.15	1.55	1.5018	0.0481
14	165	3.88	3.88	3.88	15	16.167	-1.16	1.5	1.5343	-0.034
15	165	3.88	3.88	3.88	17	16.167	0.83	1.53	1.5343	-0.004
16	165	3.88	3.88	0.00	17	17.022	-0.02	1.33	1.3626	-0.032
17	165	0.00	3.88	3.88	17	17.132	-0.13	1.8	1.8158	-0.015
18	150	5.83	1.94	5.83	8	8.140	-0.14	0.68	1.2876	-0.607
19	180	5.83	1.94	5.83	15	19.046	-4.04	1.08	1.3652	-0.285
20	180	1.94	5.83	5.83	13.7	13.97	-0.23	1.25	1.2596	-0.009
21	165	3.88	7.77	3.88	8	8.137	-0.13	0.88	0.9003	-0.020
22	150	1.94	5.83	1.94	12.9	13.166	-0.24	1.43	1.4343	-0.004
23	165	3.88	3.88	3.88	16	16.167	-0.16	1.49	1.5343	-0.044
24	165	3.88	3.88	3.88	16.5	16.167	0.33	1.5	1.5343	-0.034
25	180	1.94	1.94	1.94	19	21.968	-2.96	1.95	2.5138	-0.563
26	180	1.94	1.94	5.83	23	19.046	3.95	1.61	1.3652	0.2447
27	180	5.83	5.83	5.83	12	11.947	0.05	0.65	0.5284	0.1215
28	150	5.83	1.94	1.94	12	11.397	0.60	1.25	1.2703	-0.020
29	165	7.77	3.88	3.88	9	9.012	-0.01	0.6	0.6090	-0.009
30	180	5.83	5.83	1.94	16	15.615	0.38	1.07	1.1028	-0.032

Source	Response	S.S.	Df	M. S.	F Value	Prob > F	Sig.
Model	MOR	436.1	11	39.64	27.35	< 0.01	**
Model	M. loss	4.46	9	0.50	217.80	< 0.01	**
X1	MOR	48.12	1	48.12	33.20	< 0.01	**
X1	M. loss	0.20	1	0.20	88.74	< 0.01	**
X2	MOR	33.94	1	33.94	23.42	< 0.01	**
X2	M. loss	0.49	1	0.49	214.63	< 0.01	**
X3	MOR	42.48	1	42.48	29.31	< 0.01	**
X3	M. loss	0.12	1	0.12	54.55	< 0.01	**
X4	M. loss	0.011	1	0.011	4.73	0.0418	*
X2²	MOR	18.88	1	18.88	13.02	0.002	**
X2²	M. loss	0.18	1	0.18	79.61	< 0.01	**
X3²	MOR	13.56	1	13.56	9.36	0.001	**
X3²	M. loss	0.17	1	0.17	72.52	< 0.01	**
X4²	M. loss	0.54	1	0.54	238.70	< 0.01	**
X1x2	MOR	6.47	1	6.47	4.47	0.0488	*
X1x3	M. loss	0.012	1	0.012	5.31	0.0321	*
X2x3	MOR	37.05	1	37.05	25.56	< 0.01	**
X2x4	M. loss	0.027	1	0.027	11.95	0.0025	**
X3x4	MOR	6.47	1	6.47	4.46	0.0489	*
MOR	Lack of Fit	23.21	13	1.79	3.11	0.1091	not sig.
M. loss	Lack of Fit	0.041	15	0.00275	3.21	0.1016	not sig.

Data	RMSE		MAPE (%)		R
Data	MOR	M. loss	MOR	M. loss	MOR	M. loss
Training data	0.1545	1.2055	0.4560	0.4876	0.96919	0.95721
Testing data	0.0836	1.9914	0.0345	1.9853	0.93291	0.96552