Modelling of tensile properties of needle-punched nonwovens using artificial neural networks

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1 Indian Journal of Fibre & Textile Research Vol. 25, March 2000, pp Modelling of tensile properties of needle-punched nonwovens using artificial neural networks S Debnath, M Madhusoothanan" & V R Srinivasmoorthl Department of Textile Technology, Anna University, Chennai , Indi a Received 19 April 1999; accepted II May 1999 The m o de~ling of tensile properties of needle-punched nonwoven fabrics produced from the blends of jute and polypropylene fibres with varying fabric weight, needling density and blend ratio has been done. The tenacity and initial modulus values of needle-punched nonwoven fabrics have been predicted with the help of empirical model (using multiple regression analysis) and artificial neural networks and compared with the experimental values. The artificial neural network model is found to be much better and more accurate than an empirical model. An attempt has also been made for the experimental verification of the predicted values for extrapolated input variables. The prediction by artificial neural network model shows better results than that by empirical model even for the extrapolated input variables. Keywords: Artiticial neural network, Initial modulus, Jute/polypropylene, Modelling, Needle-punched fabric, Tenacity 1 Introduction The factors which affect the tensile properties of needle-punched fabrics are the fibre property, web characteristics and machine parameters. Fibre properties influencing the fabric tensile properties are the fibre cross- sectional shape, fibre fineness, fibre length, fibre crimp and fibre surface friction, whereas the web characteristics that influence the tensi le properties are fabric weight and type of web laying technique. Needling density, depth of needle penetration, angle of needl e punching, needle particulars, etc are the machine parameters that influence the tensile properties of needle-punched nonwovens l. Needling density and depth of needle penetration have more or less same effect on the fabric properties 2. The properties of nonwoven fabric have been studied and predicted using regression equations 2. The advantage of such a system is that an empirical model can be developed, which may be used to design a fabric with desired properties. To develop an empirical model, either a full factorial design or a statistical design may be used. Use of a statistical model decreases the number of experimental combinations. In recent days, artificial neural networks (ANN) have shown a great success for modelling non-linear "To whom all the correspondence should be add ressed. bdepartment of Mathematics, Anna University, Chennai , India. processes. Studies have been carried out to model the tensile properties of air-jet yam using ANN 3.4. ANN has also been used to model the relaxation curve of yam after dynamic loading 5. Luo and Adam 6 used the HVI test results to train the neural nets and predict the yam strength. An attempt has also been made to build models for predicting ring and rotor yarn hairiness using a back propagation ANN7. Fan and HunterS used ANN for predicting the fabric properties based on fibre, yarn and fabric constructional parameters. The literature review shows that the ANN model is a powerful and accurate tool for predicting a non-liner relationship between input and output variables. In the present study, the tenacity and initial modulus values of needle-punched nonwoven jute/polypropylene blended fabric having varying weight, needling density and blend ratio have been predicted with the help of empirical model (using multiple regression analysis) and artificial neural networks and then compared with the experimental values. An attempt has also been made for the experimental verification of the predicted values for extrapolated input variables. The polypropylene and jute fibres were selected for the study as these fibres differ in fineness and cross-sections. 2 Materials and Methods 2.1 Sample Preparation Box and Behnken design 9 for three variables was used to produce the samples. Fabric weight, needling

2 32 INDIAN J. FIBRE TEXT. RES., MARCH 2000 density and blend ratio of polypropylene and jute were varied as shown in Table I. Jute fibres (Tossa 4 grade) were treated with 18% NaOH and then blended with polypropylene fibres (0.44 tex, 80 mm) as per the work plan (Table I). The blended fibres were passed twice through the roller and clearer card, and then fed to the cross-iapper. The cross-laid webs were pre-needled at 50 punches/cm 2. The required web weight and needling density were obtained by combining the pre-needl ed webs and by varying the number of passes through the machine respectively. The web was reversed after each passage so that it was punched on both sides. The depth of needle penetration was kept constant at I I mm. 15 X 36 X RlSP, 3 Y2 X IA" X 9 needle was used for all webs. 2.2 Tenacity and Initial Modulus The tenacity and initial modulus were measured in both machine and transverse directions using an Instron tensile tester (Model 1185) as per the ASTM standard D The tenacity was calculated by normalizing the breaking load by fabric weight and width of the specimen. The initial modulus was calculated from load elongation curve. The mean of ten readings was considered. The coefficient of variation of ten reading was less than 5%. 2.3 Empirical Model An empirical equation of second order polynomi al was derived to predict the tenacity and initial modulus of the samples produced using Box and Behnken factorial design 9. Y={3o + {3 IXl+{32X2+{33X3+{3IIXI 2 +{322X2 2 +{333X3 2 +{312XI X2+{3 I3XlX3+{323X2X3... ( I) where Y is the predicted fabric property (tenacity or initial modulus); XI, the fabric weight ; X 2 the needling density; X3 the bl end rati o; {30. the constant and {3l, the coefficient of the variable Xi. The predicted fabric properties were then compared with the actual values. 2.4 Artificial Neural Network Model The physiology of neurons present in biological neural system, such as human nervous system, has been the fundamental idea in deve loping the ANNs. This computational model can be trained to capture non-linear relationship between input and output variables with scientific and mathematical basic. In recent days, the most commonly used modelling is layered feed-forward neural network with multi-layer perceptions with back propagation learning algorithms 3-5,7 The ANNs are computing systems composed of a number of highly interconnected layers of simple neurons like processing elements, which process information by the ir dynami c response to external inputs. The information passes through the complete network by linear or non-linear transformations, The weights are determined by training the neural nets. Once the ANN is trained, it can be used to predict for new sets of inputs. In this study, multilayer feed-forward neural network architecture is used (Fig. I ). The c irc le represents Table I-Experimenlal design for lhrec variables Sample Fabric wei ghl No. g/m2 (XI) II input Layer Fabric Weight Polypropylene (%) Needling density punches/cm 2 (X 2 ) Hidden Layers Blend ratio (Polypropylene: jute) (X,) 60 : : : 40 40: 60 80: 20 40: 60 80: : 60 80: 20 40: : 20 Output Layer Fig. I-Neural architecture of the tensile properties

3 DEBNATH et al. : MODELLI NG OFTENSILE PROPERTI ES OF NEEDLE-PUNCHED NONWOVENS 33 the neurons arranged in fi ve layers. One input, one output and three hidden layers have been used in the present stud y. The neuron (i) in one layer is connected with the neuron (j) in nex t layer with weights (W ij ). The data has been scaled down between 0 and I by normalizing them with their respective values. The ANN was trained by presenting it successively with fifteen sets of input-output data pairs. The input variables selected were fabric weight, needling density and blend rati o. The outputs were the tenacity and initial modulu s of the fabric. The modelling was done on both the principle directi ons. The error and correlation were calculated between the experimental and predicted values by empirical and ANN models. Table 2--Coefticient s and constants of empirical model Constant/ Tenacity Initi al modu lus coefficient Machine direction Transverse directi on Machine direct ion Transverse directi on / e-0 I I 838e-0 I /31 I I e e e e-03 i32 3. I 2902ge e e I 7e-03 / e-0 I e-0 I e c-02 / e I 6667e e e-06 / e I 67e e-05 - I c-05 / e e I 6e e-05 / e I 55000e e-06 I. I 465OOe-05 / e-05 - I. I 56625e e e-05 / e e I 625e e-05 Table 3-Weights of ANN model for tenacity and initial modulu s Weight between layers I" and 2 nu W I I W I 2 W l.1 W 21 Wn W 2:. W:' I W:' 2 W" 2 nd and 3'u W II W 12 Wn W 21 Tenacit y Machine Transverse di rection direction Initi al modulus Machine Transverse direction direction Contd.

4 34 [NO[ AN J. FIBRE TEXT. RES., MARCH 2000 Table 3-Weights or ANN model for tenacity and initial modulus---collld Weight between Tenacity [ni tial modulus layers Machine Transverse Machine Transverse direction direction direction direction Wn W W RI W W ,,1 and 4th W II W W I W Wn W W W W R th and 5 th WI{) W W Table 4---Exp~rimental and predicted tenacity valucs by empirical and ANN models Sample Tenacity in the machine direction Tenacity in the tran sverse direction No. Experimental Predicted Absolutc Experimental Predicted Absolute cn/tex cn/tex error, % cn/tex cn/tex error, % Empirical ANN Empirical ANN Empirical ANN Empirical ANN ' ! I II [ I R2 values Mean absolute percentage error SO of absolute percentage error

5 DEBNATH e/ al. : MODELLING OF TENSILE PROPERTIES OF NEEDLE-PUNCHED NONWOVENS 35 3 Results and Discussion The constants and coefficients in the empirical model, calculated with the help of multiple regression analysis, are given in Table 2. The ANN was trained up to 64,000 cycles to obtain optimum weights. The weights of ANN for tenacity and initial modulus in both machine and transverse directions are shown in Table 3. Tables 4 and 5 show the experimental values, predicted values and their prediction error for tenacity and initial modulus respectively. The ANN model shows a very good relationship (R 2 values) between the experimental and predicted tenacity values as compared to empirical model in both the principle directions. A similar trend is also observed in the case of initial modulus. The ANN model shows much lower absolute percentage error than the empirical model. The standard deviation and mean percentage error also shows a similar trend. This indicates that as compared to empirical model, the prediction by ANN model is closer to the experimental values and that the variations in error among the samples are also lower. Table 5-Experimental and predicted initial modulus values by empirical and ANN models Sample Initial modulus in machine direction Initial modulus in transverse direction No. Experimental cnltex Predicted cn/tex Absolute error % Experimental cn/tex Predicted cn/tex Absolute error % Empirical ANN Empirical ANN Empirical ANN Empirical ANN II R2 va lues Mean absolute percentage error SO of absolute percentage error Table 6--Experimental verification of predicted results Specifications Tenacit y, cn/tex Initi al modulus, cnltex Experi- Predicted by models Absolute error, % Experi- Predicted by models Absolute error, % mental Empirical ANN Empirical ANN mental Empirical ANN Empirical ANN MD TO MD TO MD TO MD - Machine direction; TO - Transverse direction

6 36 INDIAN 1. FIBRE TEXT. RES., MARCH 2000 This could be due to the fact that the prediction by empirical model is not very accurate when the relationship between the inputs and outputs is nonlinear. The ANNs and empirical models were also presented to three sets of inputs, which have not appeared during the modelling phase (Table 6). The input variables were selected in such a way that one input variable was beyond the range, with which the ANN was trained or empirical model was developed (Table I). Table 6 indicates that the prediction errors of ANNs are lower than those of empirical model in machine and transverse directions for both tenacity and initial modulus. In Table 6, the predicted values by ANN model give higher absolute percentage error than the predicted values shown in Tables 4 and 5. This may be due to the fact that the selected input variables (Table 6) are beyond the range over which the empirical or ANN models were developed. However, in most of the cases of prediction, ANNs give lesser absolute percentage error than the empirical model. 4 Conclusions The tensile properties of needle-punched nonwoven fabrics can be predicted from empirical and ANN models. Based on the experience acquired in developing these models, it may be concluded that ANNs may be used successfully for predicting the tenacity and initial modulus of needle-punched nonwoven fabrics. The ANN model has been found to be more accurate than the empirical model. Prediction of tensile properties by ANN model shows considerable lower error than empirical model even when the inputs are beyond the range of inputs, which were used for developing the model. ANNs can be used effectively even for predicting non-linear relationship between process parameters and fabric properties. Once ANNs have been trained, even shop floor people can use these for predicting the tensile property. This will minimize the time taken in developing a fabric with required fabric property. ANN can also be used for predicting the tensile properties outside the range over which it has been trained with minimum error. Acknowledgement One of the authors (SO) is thankful to the Council of Scientific and Industrial Research, New Delhi, for providing a senior research fellowsh ip. References I Rakshit A K, Desai A N & Balasubramanian N, Proceedings,3(f' joil1ltechnological conference of ATIRA, BTRA, SITRA & NITRA (ATIRA, Ahmedabad, India), 1986, Subramaniam V, Madhusoothanan M & Debnath C R, Proceedillgs,lilltenzationai collferellce on 1I0nwovens (The Textile Institute, North Indi a Section, New Del hi, Indi a), 1992, Rajamanickam R, Stcvcn Hansen S M & 1ayaraman S, Text Res J, 67( 1997) Ramesh M C, Rajamanickam R & 1ayara man S, J Text IlI st, 86 ( 1995) Vangheluwe L, Selle S & Kiekens P, J Text IIlSI, 87 (1996) Luo C & David Adams L, Text Res J, 65 ( 1995) Zhu R & Ethridge 0, Text Res J, 67 ( 1997) Fan 1 & Hunter L, Text Res J, 68( 1998) Box G E P & Behnken 0 W, Technollletrics, 2 (1960) 455.