Indian Journal of Fibre & Textile Research Vol. 38, March 2013, pp. 81-86
Prediction and optimization of mechanical properties of particles filled coir-polyester composites using ANN and RSM algorithms S Sathiyamurthy1, a, A Syed Abu Thaheer2 & S Jayabal3 1
Department of Mechanical Engineering, Dhaanish Ahmed College of Engineering, Chennai 600 301, India Department of Mechanical Engineering, Mohamed Sathak Engineering College, Kilakarai 623 806, India 3 Department of Mechanical Engineering, A C College of Engineering and Technology, Karaikudi 630 004, India 2
Received 30 August 2011; revised received and accepted 27 February 2012 Mechanical properties of coir-polyester composites filled with aluminium oxide and calcium carbonate particles have been evaluated. As the mechanical properties of coir-polyester composites mainly depend upon the fibre length, fibre diameter and filler content, the present study deals with the prediction of mechanical properties using artificial neural network and determination of optimum fibre parameters using response surface methodology algorithms. The particles filled coir-polyester composites exhibit better values of tensile strength, flexural strength, impact strength and abrasion loss properties of 21.39 MPa, 79 MPa, 37.28 kJ/m2 and 570 mm3 for 42.41 mm fibre length, 0.25mm fibre diameter and 2.5% filler content respectively. Keywords: Aluminium oxide, Artificial neural network, Calcium carbonate, Coir-polyester composites, Response surface methodology
1 Introduction Polymer composites containing natural fibre as reinforcement material have received considerable attention in the recent years. Coir fibre based composites, depending on its specific characteristics, could also find a position in production of engineering components in manufacturing sector1. The inorganic fillers are used in fibre-reinforced composites for producing the desired mould shape and to reduce the fabrication cost of the composites2. Most of the researches have been carried out on the characterization of natural fibre composites but the prediction of mechanical properties is found to be limited in literature. Some of the manufacturing studies on glass fibre-reinforced polymer composites have been carried out using fuzzy logic techniques and neuro fuzzy techniques3, 4. The mathematical modeling and optimization of drilling responses in natural coir fibre-reinforced polyester composites using response surface methodology5 and drilling responses optimization using non-conventional optimization technique (genetic algorithm)6 provided the initiative to apply soft computing techniques on the prediction of ______________ a Corresponding author. Present address: Department of Mechanical Engineering, Sri Ramanujar Engineering College, Chennai 600 127, India. E-mail:
[email protected]
mechanical properties of particles-filled natural fibre reinforced-polyester composites. In this study, the mechanical properties of aluminium oxide and calcium carbonate particlesimpregnated randomly oriented coir fibre-reinforced polyester composites have been predicted using artificial neural network approach. The optimum fibre parameters for maximum and minimum values of mechanical behaviors are also determined using response surface methodology. 2 Materials and Methods The resin system consists of unsaturated orthophthalic polyester resin, methyl ethyl ketone peroxide (MEKP) catalyst and cobalt octoate accelerator supplied by Sri Vinayaka Enterprises, Chennai, Tamilnadu, India were used. The resin, catalyst and accelerator were mixed in the ratio of 1:0.015:0.015 for preparing composite plates. The simple hand lay-up process was followed for fabricating inorganic fillers impregnated coirpolyester composites. Poly vinyl acetate (PVA) release agent was applied to the surfaces of mold before the fabrication. The inorganic fillers were purchased from Spectrum Reagents & Chemicals Private Limited, Edayar, Kerala and mixed with equal ratio (50:50) before adding to the resin system in 2% and 4% by weight.
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The coir fibres supplied from M/s Ganapathi fibres, Fibre Extraction Unit, Thamaraikulam, Pollachi, Tamilnadu, India were used. The randomly oriented organic fibres were distributed over the resin system in 24 % by weight7 and pressed heavily with load value of 500 N for one hour. After one hour, the composite was removed from the mold and cured at room temperature (29°C) for 24 h. The same procedure was followed to prepare different types of Al2O3-CaCO3-coir-polyester composites as per full factorial design matrix. Tensile behavior of inorganic fillers added coirpolyester composites was measured using universal testing machine as per ASTM D638-08 standard. The specimens were cut from the fabricated composite in the approximate length, width and thickness of 165, 25 and 3 mm respectively. Five identical specimens were tested to obtain average tensile strength value. The rectangular test piece (127×12.7×3 mm) was cut from the prepared composites for flexural strength test. Three point flexural tests were conducted as per ASTM D790-07 in Instron machine. Five identical specimens were tested to obtain average flexural strength of composites. Impact strength testing was carried out using impact testing machine as per ASTM D256-06 standard. The specimens were cut from the fabricated composite in the approximate length, width and thickness of 62.5, 6.25 and 3 mm respectively for Izod impact test. Low velocity instrumented wear loss tests were carried out on composite specimens. The tests were done as per ASTM D 5963 standard using abrasion loss testing machine. 3 Results and Discussion The fibre parameters and their levels are given in Table 1. As per full factorial design (3 parameters and 3 levels in each parameter), 27 particles-impregnated coir-polyester composites were fabricated. The results of tensile strength, flexural strength, impact strength, and abrasion loss tests are given in Table 2. 3.1 Effect of Fibre Parameters on Mechanical Properties
The very low tensile strength is obtained for 10 mm fibre length. The tensile strength increases when the fibre length varies from 30 mm to 50 mm and the maximum value of tensile strength is obtained in 50 mm fibre length for Al2O3 + CaCO3 filled coirpolyester composites. The maximum flexural properties are obtained in all the levels of fibre length.
Table 1—Fibres parameters and their levels Level Low Medium High
Fibre length mm
Fibre diameter mm
Filler content %
10 30 50
0.1 0.18 0.25
0 2 4
Table 2—Experimental results of Al2O3 + CaCO3 filled coirpolyester composites Run Fibre Fibre Filler Tensile Flexural Impact Abrasion length diameter content strength strength strength loss mm mm % MPa MPa kJ/m2 mm3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
10 10 10 10 10 10 10 10 10 30 30 30 30 30 30 30 30 30 50 50 50 50 50 50 50 50 50
0.1 0.1 0.1 0.18 0.18 0.18 0.25 0.25 0.25 0.1 0.1 0.1 0.18 0.18 0.18 0.25 0.25 0.25 0.1 0.1 0.1 0.18 0.18 0.18 0.25 0.25 0.25
0 2 4 0 2 4 0 2 4 0 2 4 0 2 4 0 2 4 0 2 4 0 2 4 0 2 4
10.36 12.11 15.36 10.66 14.63 17.65 12 12.5 14.59 14.3 18.95 21.01 18.25 18.97 17.88 18.79 25.48 28.25 17.56 13.25 16.25 19.39 18.63 22.25 20.36 18.44 30.45
50.00 55.30 57.40 54.00 53.00 57.00 59.00 57.80 60.36 60.00 61.00 63.00 56.25 65.42 58.75 83.56 83.96 84.63 65.00 69.00 71.00 73.00 70.00 72.45 75.20 78.30 89.75
27.00 28.00 30.00 31.00 26.80 29.00 32.00 30.00 31.00 33.00 37.33 36.12 38.20 36.58 38.90 40.20 35.80 36.40 35.34 36.30 37.20 37.01 36.06 35.60 33.22 31.91 33.10
640 670 685 626 660 704 630 691 756 640 670 701 612 654 676 615 676 698 553 610 687 590 634 760 574 605 770
The low value of flexural strength was obtained in 10 mm fibre length. The better value is observed in long fibre length and large fibre diameter for Al2O3 + CaCO3 filled coir-polyester composites. Figure 1 shows the relationship between fibre parameters and impact strength (kJ/m2). The maximum value of impact strength was obtained in 30 mm fibre length, 0.25 mm fibre diameter and 0% filler content. Hence, the better value is observed in medium length, larger fibre diameter and less filler content in Al2O3 + CaCO3 filled coir-polyester composites.
SATHIYAMURTHY et al.: MECHANICAL PROPERTIES OF PARTICLES FILLED COIR-POLYESTER COMPOSITES
Figure 2 shows the relationship between fibre parameters and abrasion loss (mm3). The better value is observed in high fibre length, low fibre diameter and less filler content in Al2O3 + CaCO3 filled coirpolyester composites.
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purpose is the prediction of future outcomes on the basis of other related information. It provides a measure of how well future outcomes are likely to be predicted by the model. An R2 of 1.0 indicates that the regression line perfectly fits the data. The R2 value of more than 0.8 is obtained7 in all the models (Table 4).
3.2 Nonlinear Regression Models
The statistical tool, regression analysis, helps to estimate the value of one variable from the given value of another. The mathematical relationship for correlating the responses, tensile strength (ts), flexural strength (fs), impact strength (is), and abrasion loss (al) and the considered process variables such as fibre length (fl), fibre diameter (fd) and filler content (fc) are obtained from the coefficients resulting from the Minitab 16 software output. Table 3 shows the mathematical equations for tensile strength, flexural strength, impact strength and abrasion loss models. In statistics, the coefficient of determination, (R2) is used in the context of statistical models whose main
3.3 Analysis of Variance (ANOVA)
Among the linear, 2FI, quadratic and cubic models, the quadratic model has been selected based on best fit of experimental data. The ANOVA for tensile strength model is listed in Table 4. The model F-value of 7.928142 implies that the model is significant (low value of probability > F)8. There is only a 0.01% chance that a ‘Model F-value’ of this large could occur due to noise. Values of ‘probability > F’ less than 0.05 indicates that the model terms are significant8. Values greater than 0.10 indicates that the model terms are not significant7,8. The ANOVA for flexural and Impact strength models are listed in
Fig. 1—Effect of fibre parameters on impact strength
Fig. 2—Effect of fibre parameters on abrasion loss
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Table 3—Non- linear regression models Response ts, MPa fs, MPa is, kJ/m2 al, mm3
R2 values
Mathematical model 9.276+0.527fl-37.877fd-0.850fc+1.155flfd-0.006flfc+6.345fdfc-0.009fl2+61.217fd2+0.270fc2 67.102+0.724fl-340.821fd+0.389fc+1.313flfd+0.017flfc+0.032fdfc-0.009fl2+1116.839fd2+0.039fc2 10.318+1.024fl+96.581fd+0.342fc-1.014flfd+0.001flfc-7.261fdfc-0.012fl2-140.450fd2+0.232fc2 678.526-2.044fl-103.526fd-5.229fc+1.265flfd+0.523flfc+91.272fdfc
0.81 0.81 0.89 0.84
ts −Tensile strength (MPa), fs−Flexural strength (MPa), is − Impact strength (kJ/m2), al − Abrasion loss (m3), fi − Fibre length (mm), fc − Filler content (%), and fd − Fibre diameter (mm). Table 4—ANOVA for tensile strength model Source Model fl fd fc fl fd fl fc fd fc f l2 fd2 fc2 Residual Corrected total
Sum of squares
df
Mean square
F value
p-value Prob > F
Remark
512.3057 174.2605 96.65134 96.25272 36.08621 0.7203 10.88662 83.9256 0.704089 6.9984 122.0574 634.3631
9 1 1 1 1 1 1 1 1 1 17 26
56.92285 174.2605 96.65134 96.25272 36.08621 0.7203 10.88662 83.9256 0.704089 6.9984 7.179848
7.928142 24.27078 13.46147 13.40596 5.02604 0.100322 1.516274 11.68905 0.098065 0.974728
0.0001 0.0001 0.0019 0.0019 0.0386 0.7553 0.2349 0.0033 0.7580 0.3373
Significant
Tables 5 and 6 respectively. The model F-value of 10.48123 implies that the model is significant. The model F-value of 16.2802 implies that the impact strength model is significant. The ANOVA for abrasion loss model is given in Table 7. 3.4 Artificial Neural Network Modelling
ANN establishes analytical model to solve the problem in the estimation, prediction, decision making and diagnosis. Each neuron has inputs and generates an output that can be seen as the reflection of local information that is stored in connections. The neural network has to be first trained and then tested to use for application. The training is done with MATLAB software. In this work ANN module is utilized for predicting the mechanical properties of inorganic fillers impregnated coir-polyester composites. The features fibre length, fibre diameter and filler content are the inputs and the tensile, flexural, impact and abrasion loss behaviors are the output for training the neural networks. The patterns are selected for training and testing the ANN. These selected patterns are normalized so that they lie between 0 and 1. A 3-5-7-1 Feed forward back propagation network has been selected for the training of tensile strength
values and the network is shown in Fig. 3a. The 3-5-6-1 feed forward back propagation network is selected for the training of flexural, impact and abrasion loss behaviors based on the accuracy of prediction and the network is shown in Fig. 3b. The learning process with 1000 Epochs and goal of 0 is set for the training of tensile strength values and the resultant graph is shown in Fig. 4. The performance curves for flexural, impact and abrasion loss behaviors are obtained by setting same targets in learning processes. 3.5 Validation of Results
Confirmation experiments are conducted for 8 set of conditions. The experimental values and the predicted values obtained from artificial neural network models are compared. The percentage of error is calculated using the following formula for the validation of mathematical model: % of error = [(Experimental value- Predicted value)/ Experimental value] ×100 The average absolute errors for ANN models are as follows: Tensile strength model Flexural strength model
: 1.30 % : 1.44 %
SATHIYAMURTHY et al.: MECHANICAL PROPERTIES OF PARTICLES FILLED COIR-POLYESTER COMPOSITES
Impact strength model Abrasion loss model
: 0.60 % : 0.29 %
3.6 Optimization by RSM
Response surface methodology (RSM) is the procedure for determining the relationship between various parameters with the response criteria and
exploring the effect of these process parameters on the coupled responses. Numerical optimization will search the design space, using the models created during analysis to find factor settings that meet the
Fig. 3—(a) 3-5-7-1 and (b) 3-5-6-1 feed forward back propagation networks
Fig. 4—Performance curve for tensile strength values
Table 5—ANOVA for flexural strength model Source Model fl fd fc fl fd fl fc fd fc f l2 fd2 fc2 Residual Corrected total
Sum of squares
df
Mean square
F value
p-value Prob > F
Remark
2646.871 1404.384 811.5078 81.55303 46.61213 5.658133 0.000282 79.64327 234.3501 0.14415 477.0093 3123.88
9 1 1 1 1 1 1 1 1 1 17 26
294.0968 1404.384 811.5078 81.55303 46.61213 5.658133 0.000282 79.64327 234.3501 0.14415 28.05937
10.48123 50.05046 28.9211 2.906446 1.661197 0.201649 1.01E-05 2.838384 8.351939 0.005137
< 0.0001 < 0.0001 < 0.0001 0.1064 0.2147 0.6591 0.9975 0.1103 0.0102 0.9437
Significant
Table 6−ANOVA for impact strength model Source Model fl fd fc fl fd fl fc fd fc f l2 fd2 fc2 Residual Corrected total
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Sum ofsquares
df
Mean square
F value
p-value Prob > F
Remark
328.1698 147.5161 0.619756 0.034265 27.81751 0.009075 14.2545 132.2895 3.706178 5.183202 38.07548 366.2453
9 1 1 1 1 1 1 1 1 1 17 26
36.46332 147.5161 0.619756 0.034265 27.81751 0.009075 14.2545 132.2895 3.706178 5.183202 2.239734
16.2802 65.86324 0.276709 0.015299 12.42001 0.004052 6.36437 59.0648 1.65474 2.314204
< 0.0001 < 0.0001 0.6057 0.9030 0.0026 0.9500 0.0219 < 0.0001 0.2156 0.1466
Significant
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Table 7—ANOVA for abrasion loss model Source Model fl fd fc fl fd fl fc fd fc Residual Corrected total
Sum of squares
df
Mean square
F value
p-value Prob > F
Remark
64138.11 4344.844 1387.176 50262.67 43.25592 5250.083 2252.598 12392.55 76530.67
6 1 1 1 1 1 1 20 26
10689.69 4344.844 1387.176 50262.67 43.25592 5250.083 2252.598 619.6277
17.25179 7.012023 2.238724 81.11754 0.06981 8.472964 3.635405
< 0.0001 0.0154 0.1502 < 0.0001 0.7943 0.0086 0.0710
Significant
Table 8—Optimum mechanical behavior for combination of fibre parameters Fibre length mm
Fibre diam. mm
Filler content %
Tensile strength MPa
Flexural strength MPa
Impact strength kJ/m2
Abrasion loss mm3
42.31 48.25 39.00 42.41 42.41
0.28 0.25 0.25 0.25 0.25
3.00 3.17 3.75 2.5 2.5
22.68 21.39
86.00 79.00
35.80 37.28
570.00 570.00
defined objectives. Optimization requires targets be set for one or more responses. The software (Design Expert) will generate a list of potential factor settings meeting the specified criteria. The software uses the defaults of the response range as the lower and upper limits, but those may not meet the true needs. Based on the mathematical equation formed by response surface design, parameters are optimized and the optimum values of tensile strength, flexural strength, impact strength and abrasion loss are found to be 21.39 MPa, 79 MPa, 37.28 kJ/m2 and 570 mm3 for 42.41 mm fibre length, 0.25mm diameter and 2.5% filler content respectively (Table 8). 4 Conclusion The mechanical properties of inorganic fillers impregnated coir-polyester composites are evaluated. The ANN models have been developed to predict the mechanical properties over the wide range of conditions. The ANN models can be used effectively for predicting the mechanical properties of inorganic fillers impregnated coir-polyester composites. The response surface methodology approach is effectively used to determine optimum fibre parameters for maximum and minimum values of responses in this investigation. The particles filled coir-polyester composites exhibit better values of tensile strength,
flexural strength, impact strength and abrasion loss properties of 21.39 MPa, 79 MPa, 37.28 kJ/m2 and 570 mm3 for 42.41 mm fibre length, 0.25mm diameter and 2.5% filler content respectively Acknowledgement Authors are thankful to Dr N S Balaji, Mechanical Engineering Department, Aksheyaa College of Engineering Puludivakam, for helping in experimentation and training of ANN models. References 1 Composite Applications using Coir Fibres in Sri Lanka, Final Report Project Number CFC/FIGHF/18FT (Delft University of Technology), 2003. 2 Mallick P K, Fibre Reinforced Composites-Materials, Manufacturing and Design (Marcel Dekker Inc., New York), 1993, 74. 3 Latha B & Senthilkumar V S, Reinforced Plastics Compos, 28 (2009) 951. 4 Latha B & Senthilkumar V S, Mater Manuf Process, 24 (2009) 509. 5 Jayabal S & Natarajan U, Int J Mach Machinability Mater, 9 (2011) 149. 6 Jayabal S & Natarajan U, Int J Adv Manuf Technol, 51 (2010) 371. 7 Jayabal S & Natarajan U, Int Adv Manuf Technol, 54 (5-8) (2011) 639. 8 Jayabal S, Natarajan U & Sekar U, Int J Adv Manuf Technol, 55 (1) (2011) 263.
