Predicting strength transfer efficiency of warp and weft ...nopr.niscair.res.in/bitstream/123456789/10746/1/IJFTR 35(4) 310-316... · prediction of yarn unevenness 10, and count-strength-product

Indian Journal of .Fibre & Textile Research

Vol. 35, December 2010, pp. 310-316

Predicting strength transfer efficiency of warp and weft yarns in woven fabrics

using adaptive neuro-fuzzy inference system

Zulfiqar Ali Malik & Mumtaz Hassan Malik

Department of Yarn Manufacturing, National Textile University, Faisalabad 37610, Pakistan

Tanveer Hussaina

Department of Textile Processing, National Textile University, Faisalabad 37610, Pakistan

and

Anwaruddin Tanwari

Department of Textile Engineering, Mehran University of Engineering & Technology Jamshoro, Pakistan

Received 4 February 2010; revised received and accepted 16 March 2010

The application of adaptive neuro-fuzzy inference system for the prediction of strength transfer efficiencies of warp and

weft yarns in woven fabrics has been studied. The developed neuro-fuzzy models are based on the input-output data sets of

264 woven fabric samples, out of which the data of 234 samples are used for developing the prediction models while data of

30 samples are used for models’ validation. The developed models are capable of predicting the warp and weft yarns

strength transfer efficiencies quite accurately, given the strength of constituent yarns, the fabric count and the float length.

Keywords: Fuzzy logic, Neuro-fuzzy system, Polyester-cotton blend, Strength transfer efficiency, Woven fabrics

1 Introduction Tensile strength of a woven fabric strip is not

equivalent, in most cases, to the cumulative strength

of the total number of constituent yarns in the test

direction. For example, if a fabric strip of 50mm

testing width contains ‘N’ number of longitudinal

yarns with each yarn of ‘S’ strength, the strength

of the fabric strip will not always be ‘N’×‘S’. This

is because of the fact that the number and strength

of individual yarns in the cross direction also have

a strong bearing on the fabric strip strength as well

as the fabric weave. The percentage of cumulative

strength of total number of individual longitudinal

yarns in the test direction, which is transferred to

the fabric after weaving, may be termed as

strength transfer efficiency (STE) of yarns in that

direction, and can be expressed for warp and weft

as follows:

100

direction in warp strip fabricin yarnst constituen ofstrength Cumulative

direction in warp strip fabric ofStrength (%) STEwp ×=

100

directionweftinstripfabricinyarnstconstituenofstrengthCumulative

directionweftinstripfabricofStrengthSTEwt(%) ×=

According to Taylor1, strength of woven fabrics of

common constructions usually ranges between 85%

and 125% of the integral strength of all the yarns in the direction tested. It is of great importance for fabric developers to be able to know that how much of the strength of individual yarns will translate into the fabric strength in a particular direction after weaving in a specific woven structure. Experiments have shown that

there is no linear relationship between the fabric strip strength and the cumulative strength of the constituent yarns. Majumdar et al.

2 proposed a model to study the

effect of warp strength, weft count and weft thread spacing on fabric strength. Debnath

3 reported that the

breaking loads at higher weft linear densities are

greater in relation to the breaking loads of the single yarns than those at the lower linear densities. Booth

4

stated that a general equation to relate the strength of the fabric and the strength of the component yarns will no doubt be complex when finally derived. Uttam and Gangwar

5 studied the effect of single yarn strength on

tensile strength of cotton terry towels and terry fabrics.

—————— a To whom all the correspondence should be addressed.

E-mail: [email protected]

MALIK et al.: PREDICTING STRENGTH TRANSFER EFFICIENCY OF YARNS IN WOVEN FABRICS

311

He stated that the relationship of strength of fabric to construction is too complex to be expressed in a single formula.

Adaptive neuro-fuzzy inference systems (ANFIS)

have been used successfully in the past for modelling

non-linear relationships between input and output

variables. Majumdar et al.6, 7

developed ANFIS based

models for the prediction of cotton yarn strength. Ucar

and Ertugral8 developed such a system for predicting

circular knitting machine parameters for cotton plain

fabrics. ANFIS based models have also been used for

modelling subjective assessment of knit fabric9, for the

prediction of yarn unevenness10

, and count-strength-

product of the yarn11

. Using a given input-output data

set, adaptive neuro-fuzzy inference system (ANFIS)

constructs a fuzzy inference system (FIS) using back-

propagation algorithm or a hybrid learning algorithm,

whereby the fuzzy system learns from the data. The

present study was undertaken to develop an adaptive

neuro-fuzzy model for the prediction of strength

transfer efficiency of warp and weft yarns in woven

fabrics using empirical data based on a carefully

manufactured range of woven fabrics under controlled

conditions using a systematic selection of yarns.

2 Materials and Methods One hundred and thirty two (132) fabric samples

each in plain weave (float length=1) and twill weave

(float length=3) were woven from 52:48

polyester/cotton blended yarns on projectile weaving

machine (P 7150). The statistics of the woven fabrics

is given in Table 1. Specific constructions of the

fabrics (117 each in plain and 3/1 twill weave) used

for developing the prediction model are given in

Table 2. Fabrics construction (15 each in plain and

3/1 twill weave) used for the validation of the

prediction models are given in Table 3.

After enzymatic desizing, all the fabric samples

were first preconditioned at a temperature of 47°C

and relative humidity of 10-25% for 4 h in a hot air

oven and then conditioned for 24 h in standard

atmosphere according to ISO 139:2005. Test

specimens were prepared and tensile strength was

determined both in warp and weft directions

according to ISO standard test method 13934-1.

Calibrated universal strength tester was used to

determine the fabric tensile strength at a gauge length

of 200 mm and an extension speed of 100 mm/min.

Out of a total number of 264 samples, the data of

234 samples were used for developing the prediction

models while data of 30 samples (15 plain weave + 15

twill weave) were used to check the validity of the developed models.

Neuro-fuzzy modelling was done with the help of

Fuzzy Logic Toolbox of MATLAB® software

(The MathWorks, Inc.) using triangular membership

functions of input variables and Sugeno-type fuzzy

inference system. Input variables used were warp

and weft single-yarn strengths, ends per 25mm,

picks per 25mm and float size (1 for plain weave,

and 3 for 3/1 twill). The output variables were

strength transfer efficiencies (STE%) of both warp

and weft yarns.

3 Results and Discussion

3.1 Development of ANFIS Prediction Models

The developed ANFIS prediction models consist of

162 rules each for warp and weft yarns strength

transfer efficiency. Each input variable consists of

3 triangular membership functions except the float

length (which consists of 2 triangular membership

functions) and output variables which consist of

162 linear membership functions. Figures 1-4 depict

the relationship between the input predictor variables

and the output variables as determined by the ANFIS

models. It is clear from the figures that the

relationship between the input and the output

variables is not linear in almost all the cases. Figures

1-3 show that both warp and weft yarns strength

transfer efficiencies (STE’s) decrease with the

increase in float length, i.e. yarn strength transfer

efficiency is higher for plain weave and lower for

twill weave. Due to higher yarn interlacements,

contact friction between warp and weft yarns of plain

fabric is more than that of twill fabric. This higher

friction provides more resistance to tensile load

resulting in higher STE for plain fabrics.

Table 1—Statistics of yarn and fabric properties

Yarn/fabric property Minimum Maximum Mean

Warp yarn count, tex 15 25 20

Weft yarn count , tex 15 25 20

Warp yarn BF, cN 272.5 537.9 405.2

Weft yarn BF, cN 272.5 537.9 405.2

Ends per 25 mm 40 80 60

Picks per 25 mm 40 80 60

Fabric warp BS, N 154 845 499.5

Fabric weft BS, N 150 823 486.5

Float length 1 3 2

Warp yarn STE, % 68 107 87.5

Weft yarn STE, % 67 101 84

BF—Breaking force, BS—Breaking strength, STE—Strength

transfer effiiciency

INDIAN J. FIBRE TEXT. RES., DECEMBER 2010

312

Figure 1 shows different trends of STE in both warp and weft directions as well as for both weave designs when warp yarn strength is increased. For plain weave, warp-way STE decreases slightly at first and then increases with the increase in individual warp yarn strength. After that it remains constant for further increase in warp yarn strength before a slight decrease in the end. At initial stages of increase in warp yarn strength (i.e. linear density), there is a negative influence of crimp interchange. A further increase in warp yarn strength (due to higher yarn linear density) results in jamming of yarns which increases STE. However, further increase in warp yarn strength due to even higher linear density results in overcrowding of the yarns which reduces the STE.

Table 2—Fabric constructions (plain and 3/1 twill) used for developing the prediction model

Sl

No.

Warp

count

tex

Weft

count

tex

Ends/

25mm

Picks/

25mm

Sl

No.

Warp

count

tex

Weft

count

tex

Ends/

25mm

Picks/

25mm

Sl

No.

Warp

count

tex

Weft

count

tex

Ends/

25mm

Picks/

25mm

1 15 15 40 40 40 20 15 40 40 79 25 15 40 40 2 15 15 50 40 41 20 15 50 40 80 25 15 50 40 3 15 15 50 50 42 20 15 50 50 81 25 15 50 50 4 15 15 60 40 43 20 15 60 40 82 25 15 60 40 5 15 15 60 50 44 20 15 60 50 83 25 15 60 50 6 15 15 60 60 45 20 15 60 60 84 25 15 60 60 7 15 15 70 40 46 20 15 70 40 85 25 15 70 40 8 15 15 70 60 47 20 15 70 50 86 25 15 70 60 9 15 15 70 70 48 20 15 70 70 87 25 15 70 70 10 15 15 80 40 49 20 15 80 40 88 25 15 80 40 11 15 15 80 50 50 20 15 80 60 89 25 15 80 50 12 15 15 80 70 51 20 15 80 70 90 25 15 80 60 13 15 15 80 80 52 20 15 80 80 91 25 15 80 80 14 15 20 40 40 53 20 20 40 40 92 25 20 40 40 15 15 20 50 40 54 20 20 50 40 93 25 20 50 40 16 15 20 50 50 55 20 20 50 50 94 25 20 50 50 17 15 20 60 40 56 20 20 60 40 95 25 20 60 40 18 15 20 60 50 57 20 20 60 50 96 25 20 60 50 19 15 20 60 60 58 20 20 60 60 97 25 20 60 60 20 15 20 70 40 59 20 20 70 40 98 25 20 70 40 21 15 20 70 50 60 20 20 70 60 99 25 20 70 50 22 15 20 70 70 61 20 20 70 70 100 25 20 70 70 23 15 20 80 40 62 20 20 80 40 101 25 20 80 40 24 15 20 80 60 63 20 20 80 50 102 25 20 80 50 25 15 20 80 70 64 20 20 80 60 103 25 20 80 70 26 15 20 80 80 65 20 20 80 80 104 25 20 80 80 27 15 25 40 40 66 20 25 40 40 105 25 25 40 40 28 15 25 50 40 67 20 25 50 40 106 25 25 50 40 29 15 25 50 50 68 20 25 50 50 107 25 25 50 50 30 15 25 60 40 69 20 25 60 40 108 25 25 60 40 31 15 25 60 50 70 20 25 60 50 109 25 25 60 50 32 15 25 60 60 71 20 25 60 60 110 25 25 60 60 33 15 25 70 40 72 20 25 70 40 111 25 25 70 40 34 15 25 70 60 73 20 25 70 50 112 25 25 70 60 35 15 25 70 70 74 20 25 70 70 113 25 25 70 70 36 15 25 80 40 75 20 25 80 40 114 25 25 80 40 37 15 25 80 50 76 20 25 80 50 115 25 25 80 60

38 15 25 80 60 77 20 25 80 70 116 25 25 80 70 39 15 25 80 80 78 20 25 80 80 117 25 25 80 80

Table 3—Fabric constructions (plain and 3/1 twill) used for validation

of the prediction model

Sl No Warp count, tex Weft count, tex Ends/25mm Picks/25mm

1 15 15 70 50

2 15 15 80 60

3 15 20 70 60

4 15 20 80 50

5 15 25 70 50

6 15 25 80 70

7 20 20 70 50

8 20 20 80 70

9 20 25 80 60

10 25 15 70 50

11 25 15 80 70

12 25 20 70 60

13 25 20 80 60

14 25 25 70 50

15 25 25 80 50


313

For twill weave, there is a gradual increase in

warp-way STE with the increase in warp yarn

strength (i.e. yarn linear density) up to a point and

then a gradual decrease with the increase in individual

warp yarn strength. Twill weave has loose structure

(i.e. longer floats) as compared to plain weave;

therefore warp-way STE increases with the increase

in yarn linear density up to a point after which the

negative impact of crimp percentage comes into play,

reducing the STE.

Figure 1(b) depicts the influence of warp yarn

strength on weft-way STE. When weave design is

plain, weft-way STE is first reduced due to increase in

warp strength and is then increased due to further

increase in the crossing yarn strength. The initial

decrease in STE can be explained by the negative

influence of the crimp interchange and the later

increase in STE with the increase in warp strength can

be explained by the jamming effect of the crossing

yarns due to higher linear density. In case of twill

design, there is no change in weft-way STE with the

increase in crossing yarn strength at start but further

increase improves the weft-way STE. With the yarns

of lower strength (smaller diameter), there may be

little negative influence of the crimp interchange

because of longer floats and looser fabric structure.

However, with yarns of higher strength (i.e. of larger

diameter), the fabric structure becomes tighter and the

jamming effect of yarns comes into play, which

enhances the weft-way STE.

Figure 2(a) shows the effect of weft yarn strength on warp-way STE. In case of plain weave, warp-way STE is increased quite sharply at first due to increase in weft or crossing yarn strength and then less sharply

on further increase in the crossing yarn strength. The sharp increase may be attributed to yarn jamming while the gradual increase in STE at higher yarn strength (i.e. higher yarn linear densities) may be attributed to overcrowding of the yarns because of further increase in diameter of the yarns. In case of twill design, there is increase in warp STE with the increase in crossing weft yarn strength at start but further increase in weft yarn strength reduces the warp STE. This may be explained by the fact that initially the positive effect of increase in diameter of the crossing yarns outweighs the negative effect of crimp interchange. However, with further increase in the diameter of the crossing yarns, the negative effect of crimp interchange dominates the positive influence of the increase in yarn diameter.

Figure 2 (b) shows the effect of weft yarn strength on the weft-way STE. For plain weave, weft-way STE decreases at first and then increases with the increase in individual weft yarn strength. Actually, the increase in weft yarn strength is due to increase in the linear density of the yarn, which, in turn, is due to increase in the diameter of the yarn. Decrease in weft-way STE at start is due to negative impact of crimp interchange. Further increase in weft yarn strength results in the jamming of the yarns, providing a sharp rise in weft-way STE. For twill weave, there is an increase in weft-way STE at first and further increase in individual weft yarn strength cannot generate positive influence. Actually, twill weave has loose structure as compared to plain. Thus, first part of trend is independent from any negative impact of crimp interchange and hence the increase in weft STE is observed, while second part is under the negative impact of crimp interchange which reduces the STE.

(a) (b)

Fig. 1—Effect of warp yarn strength and float size on warp (a), and weft (b) STE


314

A difference in trends between warp-way and weft-way STE also merits some explanation. When warp and weft yarns are interlaced on a weaving machine, warp yarns remain under higher tensions as compared to weft yarns. This tension difference creates different amount of crimp in both yarn systems even when warp and weft yarn linear densities and number of threads per unit length are same. Due to this reason, behavior of weft-way STE varies from warp-way STE up to the jamming stage. At jamming stage, the behavior of weft-way STE becomes similar to that of warp-way STE.

Figure 3 (a) indicates the effect of increase in number of cross yarns / 25mm (from 40 threads/ 25 mm to 80 threads/25 mm) on warp-way STE. For

plain weave, rise in warp-way STE is obtained when pick density is changed from 40 to 50 and then 50 to 60. However, the rate of increase accelerates when pick density is increased from 60 to 70 and then from 70 to 80. First step shows a crimp interchange stage while second step shows a jamming stage where the effect of increasing the number of picks per 25 mm is very prominent. Same trend is observed for twill weave. However, the increase in STE is less sharp because of longer float size.

Figure 3 (b) shows the effect of increase in pick density on weft-way STE. For plain weave, very slight increase in weft way STE is obtained when pick density is increased from 40 to 60, however it rises

(a) (b)

Fig. 2—Effect of weft yarn strength and float size on warp (a), and weft (b) STE

(a) (b)

Fig. 3—Effect of picks per 25mm and float size on warp (a), and weft (b) STE


315

very sharply after pick density of 60 when the yarn jamming stage is reached. For twill weave, STE decreases with the increase in pick density, which shows a negative influence of crimp interchange.

Figure 4 shows the influence of pick density and warp yarn strength on warp-way and weft-way STE. It is evident from figure that at minimum yarn strength and highest pick density, the warp-way STE is highest but when both factors are at highest level, STE is reduced. This highest STE shows the stage of jamming structure and further increase in yarn diameter transfers this jamming stage to overcrowding, and consequently the reduction in STE is observed.

It can be observed from Fig. 4 (b) that at medium yarn strength and highest pick density, weft-way STE is highest but when both factors are at highest level, STE is reduced. This highest STE shows the stage of jamming structure and further increase in yarn diameter transfers this jamming stage to overcrowding and consequently reduction in STE is attained. It can be said that jamming stage is attained at minimum yarn strength in case of warp-way STE while at medium yarn strength in case of weft-way STE.

Overall, graphical relationships between input and

output variables, as determined by the ANFIS model,

are very transparent and easily understandable. Even a

cursory look at Figs 1-4 underlines the non-linear nature

of warp and weft yarns strength transfer efficiency. This

is why the ANFIS models are best at exposing such non-

linear relationships.

3.2 Validation of ANFIS Prediction Models

As mentioned above, out of a total number of

264 woven fabric samples, the data of 234 samples were

used for developing the prediction models while data of

30 samples (15 plain weave + 15 twill weave) were

used to check the validity of the developed models.

The validation results of the ANFIS models for the

training data sets and the unseen validation data sets

are given in Tables 4 and 5. It can be observed that

neuro-fuzzy models have very good prediction

accuracy in terms of the correlation coefficients

between the actual and the predicted values, mean

absolute error (%) as well as the maximum error (%).

4 Conclusions

Warp and weft yarns strength transfer efficiencies have been successfully predicted for plain and twill

(a) (b)

Fig. 4—Effect of picks per 25mm and warp yarn strength on warp (a), and weft (b) STE

Table 5—Validation results of neuro-fuzzy models using

validation data set of 30 samples

Strength transfer efficiency Statistical parameter ANFIS results

Correlation coefficient 0.951

Mean absolute error, % 1.1

Warp yarns

Maximum error, % 3.8



Weft yarns


Table 4—Validation results of neuro-fuzzy models using

training data set of 234 samples

Strength transfer efficiency Statistical parameter ANFIS results



Warp yarns




Weft yarns



316

weaves by an adaptive neuro-fuzzy system. The performance of adaptive neuro-fuzzy system has been found to be very good. The developed fuzzy rules not only provide clear understanding about the relationships between different input and output variables but the rule-base is also linguistically better interpretable and always extendable.

Acknowledgement

The authors are thankful to the Higher Education Commission, Pakistan for providing funds to carry out this research work. The authors are also grateful to Al-Karam Textile Mills Karachi, Al-Rehmat Textile Mills Faisalabad as well as Clariant Pakistan Ltd for providing support in warping and sizing, fabric tensile testing and desizing chemicals respectively during the study.

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Panigrahi D and Biswas A, Fiber Polym, 9 (2008) 240.

3 Debnath C R, J Text Inst, 79 (1988) 262.

4 Booth J E, Principles of Textile Testing, 3rd edn

(Butterworths, England), 1982.

5 Uttam M & Gangwar A K S, Man Made Text India, 49

(2006) 30.

6 Majumdar A, Majumdar P K & Sarkar B, J Text Inst, 96

(2005) 55.

7 Majumdar A, Majumdar P K, & Sarkar B, Indian J Fibre

Text Res, 30(2005) 19.

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Predicting strength transfer efficiency of warp and weft ...nopr.niscair.res.in/bitstream/123456789/10746/1/IJFTR 35(4) 310-316... · prediction of yarn unevenness 10, and count-strength-product