Fibers and Polymers 2014, Vol.15, No.2, 390-395

390

Comparison of Regression and Adaptive Neuro-fuzzy Models for Predicting the

Compressed Air Consumption in Air-jet Weaving

Tanveer Hussain*, Abdul Jabbar, and Shakeel Ahmed1

Faculty of Engineering & Technology, National Textile University Faisalabad, Pakistan1Be Be Jan Colors (Weaving Unit) Faisalabad, Pakistan

(Received April 26, 2013; Revised July 16, 2013; Accepted July 17, 2013)

Abstract: The aim of this study was to compare the response surface regression and adaptive neuro-fuzzy models forpredicting the compressed air consumption in air jet weaving. The prediction models are based on the experimental data of100 samples comprising weft yarn count, fabric width, loom speed and reed count as input variables and compressed airconsumption as output/response variable. The models quantitatively characterize the linear and quadratic relationships aswell as interactions between the input and output variables exhibiting very good prediction ability and accuracy, with ANFISmodel being slightly better in performance than the regression model. The models could be used for estimating thecompressed air consumption, identifying air leakages and production planning in a weaving mill.

Keywords: Air-jet weaving, Compressed air, Prediction, Regression, ANFIS

Introduction

Woven fabrics are commonly produced on a weaving

machine known as “loom”. Among the different types of

looms available on the basis of weft insertion mechanism,

air jet looms have gained wide acceptance in the textile

industry due to their high production rate. However, a major

disadvantage of air jet looms is the high energy cost of

compressed air production for use in the weft insertion.

The researchers and machine manufacturers have done

great efforts to reduce air consumption to some extent

without compromising the loom performance and fabric

quality. To improve the efficiency of the air-jet during weft

insertion and to reduce air consumption, a better air jet

nozzle design was developed by Ishida and Okajima [1,2],

and also by Jeong et al. [3]. Göktepe and Bozkan investigated

the possibility to reduce the air consumption on air jet looms

without any new investment [4]. More than 20 % decrease in

the air consumption was attained by decreasing the hole

diameter of single-holed relay nozzle and by optimizing the

blowing time of multi-holed relay nozzles. Masuda et al. [5]

developed a new auxiliary device, which mechanically

supported the weft insertion, for the reduction of compressed

air consumption. The energy consumption of the compressed

air was found to be reduced from 10 to 30 % with the help of

this new device.

Belforte et al. [6] investigated the effect of different sub-

nozzle geometries on pneumatic weft insertion and compared

their performance with constant air consumption. It was

revealed that single-hole nozzles give best performance

instead of porous nozzles. The pneumatic weft insertion is

affected by different weft yarn properties as investigated by

some researchers [7,8]. The yarn linear density, structure and

twist were considered the main factors affecting the

suitability of pneumatic weft insertion and the effect of twist

direction in yarn was not found to be significant on air-jet

weft insertion. The yarn velocity in the weft insertion

channel increases with the increase in number of filaments

due to the larger yarn surface in contact with the air [9]. The

effect of the yarn twist coefficient on weft yarn speed at

different yarn linear densities was determined by Kayacan et

al. [10] using fuzzy logic. It was concluded that high yarn

twist coefficient reduces the yarn speed and increases the

insertion time and increase in yarn count increases the speed

of weft yarn. Githaiga et al. [11] studied the influence of

fibre properties, rotor yarn production parameters and yarn

properties on the weft yarn insertion speed. The fibre quality

and yarn production parameters were found to have significant

influence on the weft yarn insertion speed and with respect

to the yarn properties, high correlation coefficients were

obtained between the experimental and the predicted values

of the yarn speed.

Despite several endevours for the optimization and reduction

of compressed air consumption on air jet looms, there is

paucity of reported studies on modeling the effect of various

weaving parameters on the compressed air consumption,

which has the major bearing on the energy cost in an air jet

weaving mill. The aim of this study is to compare the

regression and adaptive neuro-fuzzy models (ANFIS) for

predicting the compressed air consumption in air jet looms.

Such a model may be useful to optimize the production

planning in the weaving shed taking into account the

compressed air generation capacity of the plant and the

styles of fabrics to be woven at a time. The model may also

be useful in identifying compressed air wastages in a

weaving shed by comparing the actual air consumption to

that predicted by the model developed under controlled

production conditions with no air leakages. Furthermore, the*Corresponding author: hussain.tanveer@gmail.com

DOI: 10.1007/s12221-014-0390-x

Predicting the Compressed Air Consumption in Air-Jet Weaving Fibers and Polymers 2014, Vol.15, No.2 391

model may also be used for estimating the share of air

consumption cost for a particular style of fabric. The ANFIS

approach has been successfully used in the past for modeling

non-linear relationships in textiles [12-17].

Materials and Methods

One hundred and eight (108) fabric samples were woven

on Picanol Omni-plus air jet loom, using four different weft

yarn counts (10, 20, 40, 80 Ne), three different fabric widths

(63, 95, 120 inches), three different loom speeds (450, 550,

650 R.P.M.), and three different reed counts (16, 28.75, 53.5

dents per inch). A summary of weaving factors and their

levels is given in Table 1. The warp yarn count (40 Ne),

number of ends per inch (108), picks per inch (84) and the

fabric weave design (plain) were kept same for all the

samples. The properties of weft yarns used in the study are

given in Table 2.

The compressed air consumption on the air jet loom for

weaving each fabric sample was measured using TR-7900

air flow meter by Takayama Reed Company Limited. The

air flow meter, shown in Figure 1, was connected in series

with the compressed air supply pipe and the loom and the air

flow rate was measured for all the 108 trials for various

settings of input variables. The air flow meter has a

measuring range of 0 to 35 liters per second. The loom was

thoroughly checked before weaving the samples to make

sure that there were no air leakages. The main and sub

nozzles pressure were kept at 5 and 3 bars respectively, for

pneumatic weft insertion.

Out of the total 108 woven samples, the data of 100

samples were used for developing the models while the

remaining data of 8 samples were used for validating the

developed model. The regression model was developed

using MINITABTM statistical software package whereas

neuro-fuzzy modeling was done with the help of Fuzzy

Logic Toolbox of MATLABTM software using triangular

membership functions of input variables and Sugeno-type

fuzzy inference system. The input variables were weft yarn

count (X1), fabric width (X2), loom speed (X3) and reed count

(X4), while the output variable was compressed air consumption

(Y). The number and type of membership functions were

determined by trial and error to get the model with good data

fit and prediction accuracy. Grid partition method was used

for generating the fuzzy inference system (FIS) while hybrid

optimization method was used for the training of the FIS.

Error tolerance and number of training epochs were set at 0

and 6, respectively.

Results and Discussion

The Regression Model

The compressed air consumption results at different levels

of input variables are given in Table 3. These data were used

for developing the statistical model using response surface

regression, where X1 (weft yarn count, Ne), X2 (fabric width,

inches), X3 (loom speed, rpm), X4

(reed count, dents/inch) are

the input variables and Y (Compressed air consumption,

liters per second) is the output variable. The analysis of

variance (ANOVA) of response surface regression is given

in Table 4. P-values of 0.000 indicate significant linear,

square and interaction effects of the selected variables on the

air consumption. The response surface regression coefficients

for air consumption are given in Table 5. Only the terms

with P-values less than 0.05 were considered statistically

significant with 95 % confidence level. Any term with P-

values greater than 0.05 was excluded during analysis. The

coefficient values, given in Table 5, provide a clear estimation

of the effect of input variables on air consumption. It is clear

from the coefficient values that fabric width has the maximum

effect on the air consumption, followed by loom speed, reed

Table 1. Weaving factors and their levels

Sr.

no.Factor

Factor

symbol

Factor levels

1 2 3 4

1 Weft yarn count (Ne) X1 10 20 40 80

2 Fabric width (inches) X2 63 95 120 -

3 Loom speed (rpm) X3 450 550 650 -

4 Reed count (dents/inch) X4 16 28.75 53.5 -

Table 2. Properties of weft yarns

Nominal count (Ne) 10 20 40 80

Actual count (Ne) 10.25 20.2 40.22 80.16

Count lea strength product 2516 2593 2904 3995

Twist Multiplier 4.05 3.74 4.52 4.02

CVm (%) 9.10 11.9 10.75 10.84

Thin places -50 % (km) 0 13 3 9

Thick places +50 % (km) 276 132 64 27

Neps +200 % (km) 130 204 78 133

Hairiness index 12.30 8.16 4.50 3.07

Figure 1. Air flow meter.

392 Fibers and Polymers 2014, Vol.15, No.2 Tanveer Hussain et al.

Table 3. Data for compressed air consumption used for developing the prediction models

Sr.

no.

Weft

count,

X1 (Ne)

Fabric

width,

X2 (inches)

Loom

speed,

X3 (rpm)

Reed count,

X4

(dents/inch)

Air

consumption,

Y (liters/sec)

Sr.

no.

Weft

count,

X1 (Ne)

Fabric

width,

X2 (inches)

Loom

speed,

X3 (rpm)

Reed count,

X4

(dents/inch)

Air

consumption,

Y (liters/sec)

1 10 63 450 16.00 14.3 51 40 63 450 28.75 13.5

2 10 63 450 28.75 14.1 52 40 63 450 53.50 13.1

3 10 63 450 53.50 13.8 53 40 63 550 16.00 14.3

4 10 63 550 16.00 15.0 54 40 63 550 28.75 14.1

5 10 63 550 53.50 14.6 55 40 63 550 53.50 14.0

6 10 63 650 16.00 15.8 56 40 63 650 16.00 15.0

7 10 63 650 28.75 15.6 57 40 63 650 28.75 14.8

8 10 63 650 53.50 15.5 58 40 63 650 53.50 14.6

9 10 95 450 16.00 22.6 59 40 95 450 16.00 21.8

10 10 95 450 28.75 22.1 60 40 95 450 28.75 20.8

11 10 95 450 53.50 19.6 61 40 95 450 53.50 18.3

12 10 95 550 16.00 24.1 62 40 95 550 16.00 23.5

13 10 95 550 28.75 23.5 63 40 95 550 28.75 22.3

14 10 95 550 53.50 20.3 64 40 95 650 16.00 24.6

15 10 95 650 16.00 25.5 65 40 95 650 28.75 23.8

16 10 95 650 28.75 24.8 66 40 95 650 53.50 20.8

17 10 120 450 16.00 29.3 67 40 120 450 16.00 28.6

18 10 120 450 28.75 29.1 68 40 120 450 28.75 28.5

19 10 120 450 53.50 28.8 69 40 120 450 53.50 26.6

20 10 120 550 16.00 30.8 70 40 120 550 16.00 30.1

21 10 120 550 28.75 30.5 71 40 120 550 28.75 29.8

22 10 120 550 53.50 30.0 72 40 120 550 53.50 28.1

23 10 120 650 16.00 32.3 73 40 120 650 16.00 31.6

24 10 120 650 28.75 31.8 74 40 120 650 28.75 31.2

25 10 120 650 53.50 31.1 75 40 120 650 53.50 30.1

26 20 63 450 16.00 14.0 76 80 63 450 16.00 13.5

27 20 63 450 28.75 13.8 77 80 63 450 28.75 13.3

28 20 63 450 53.50 13.5 78 80 63 450 53.50 13.0

29 20 63 550 16.00 14.7 79 80 63 550 16.00 14.1

30 20 63 550 28.75 14.5 80 80 63 550 28.75 13.8

31 20 63 550 53.50 14.3 81 80 63 550 53.50 13.8

32 20 63 650 16.00 15.3 82 80 63 650 16.00 14.8

33 20 63 650 28.75 15.1 83 80 63 650 28.75 14.6

34 20 95 450 16.00 22.3 84 80 63 650 53.50 14.5

35 20 95 450 28.75 21.3 85 80 95 450 16.00 21.6

36 20 95 550 16.00 23.8 86 80 95 450 28.75 20.5

37 20 95 550 28.75 22.8 87 80 95 450 53.50 17.8

38 20 95 550 53.50 19.8 88 80 95 550 28.75 22.0

39 20 95 650 16.00 25.1 89 80 95 550 53.50 19.0

40 20 95 650 28.75 24.3 90 80 95 650 16.00 24.3

41 20 95 650 53.50 21.3 91 80 95 650 28.75 23.5

42 20 120 450 16.00 29.0 92 80 95 650 53.50 20.4

43 20 120 450 28.75 28.8 93 80 120 450 16.00 28.5

44 20 120 450 53.50 27.0 94 80 120 450 53.50 25.8

45 20 120 550 16.00 30.5 95 80 120 550 28.75 29.6

46 20 120 550 53.50 28.5 96 80 120 550 53.50 26.6

47 20 120 650 16.00 32.0 97 80 120 550 16.00 29.8

48 20 120 650 28.75 31.5 98 80 120 650 16.00 31.5

49 20 120 650 53.50 30.6 99 80 120 650 28.75 31.0

50 40 63 450 16.00 13.7 100 80 120 650 53.50 27.5

Predicting the Compressed Air Consumption in Air-Jet Weaving Fibers and Polymers 2014, Vol.15, No.2 393

count and weft yarn count respectively. The negative sign

shows the inverse effect of a given factor on response

variable. All the four terms have confidence levels of 100 %

(P-value=0.000). The value of coefficient of determination

(R-Sq.) is 99.25 % which is a quite good figure for the

expected prediction accuracy of the regression model. The

regression equation which can be used to predict the air

consumption on air-jet loom by using actual values of

predictor variables, is given as follows:

Y=4.65617−0.0124985X1 +0.0427675X2−0.000740679X3

+0.0386741X4 +0.000270468X1

2+0.00101376X2

2

−0.000207222X1X2 −0.000362534X1X4

+0.000134115X2X3 −0.000830807X2X4 (1)

The ANFIS Model

The same data set that was used for developing the regression

model was used for developing the ANFIS model. Figure 2

shows the structure of the developed adaptive neuro-fuzzy

inference system (ANFIS). The ANFIS structure consists of

one input variables, viz., weft count with 2 triangular

membership functions (MF), and three input variables with

3 triangular MF, viz, fabric width, loom speed and reed

count. There is one output variable i.e., compressed air

consumption, consisting of 54 linear membership functions.

The whole model is based on 54 if-then rules of the form.

If w is A1 and x is B1 and y is C1, and D1 is z then output =

k1w + k2x + k3y + k4z + k5

where w, x, y and z are inputs, A, B C and D are fuzzy

membership functions (MF) for corresponding inputs, and

k1, k2, k3, k4 and k5 are constants determined by training the

model. The number and type of membership functions for

different inputs were determined through trial and error to

result in a model with good fit and prediction accuracy of

unknown input values.

The effect of fabric width and weft count on the compressed

air consumption is shown in Figure 3. The effect of fabric

width is far greater as compared to that of the weft yarn

count. For greater fabric width, more air is required for the

weft insertion as the latter has to travel longer distance along

the fabric width. Similarly higher amount of air is required

for inserting coarser/heavier weft yarn as compared to the

finer/lighter yarns. Figure 4 depicts the effect of reed count

and loom speed on the compressed air consumption. It is

evident that the air consumption increases with increase in

loom speed. This is because number of picks inserted per

unit time increase at higher loom speed, resulting in increase

in compressed air consumption per unit time. The increase in

reed count results in decrease in compressed air consumption.

This may be due to the fact that increase in dents per inch

lowers the chance of air dispersion and wastage through the

reed dents.

Table 4. Analysis of variance (ANOVA) for air consumption

Sr. no. Source DF Seq SS coefficient Adj SS Adj MS F P

1 Regression 10 4185.03 4185.03 418.50 1181.26 0.000

2 Linear 4 4147.55 3738.76 934.69 2638.26 0.000

3 Square 2 16.80 15.89 7.95 22.43 0.000

4 Interaction 4 20.67 20.67 5.17 14.59 0.000

5 Residual error 89 31.53 31.53 0.35

6 Total 99 4216.56

Figure 2. ANFIS structure.

Table 5. Regression coefficients of air consumption by response

surface analysis (coded units)

Sr.

no.Terms Coefficients

SE

coefficientT P

1 Constant 20.6662 0.14493 142.592 0.000

2 X1 -0.6900 0.07938 -8.693 0.000

3 X2 7.5198 0.07543 99.689 0.000

4 X3 1.1531 0.07237 15.932 0.000

5 X4 -1.0061 0.07448 -13.508 0.000

6 X1

2 0.3313 0.14651 2.261 0.026

7 X2

2 0.8234 0.13021 6.324 0.000

8 X1 X2 -0.2067 0.09445 -2.189 0.031

9 X1 X4 -0.2379 0.09325 -2.551 0.012

10 X2 X3 0.3822 0.08776 4.356 0.000

11 X2 X4 -0.4440 0.08567 -5.182 0.000

S=0.595217, R-Sq=99.25 %, R-Sq(pred)=99.05 %, R-Sq(adj)=

99.17 %.

394 Fibers and Polymers 2014, Vol.15, No.2 Tanveer Hussain et al.

Validation of the Prediction Models

Out of 108 samples, 8 were used to check the validity of

the developed models. A comparison of actual air consumption

values and those predicted by the developed regression and

ANFIS models is shown in Table 6. Figure 5 shows the fitted

line plot between the actual and predicted air consumption

values by the developed models. The Pearson correlations

between the actual and the predicted air consumption by the

regression and ANFIS models were found to be 0.986 (P-

value 0.000) and 0.998 (P-value 0.000) respectively, indicating

a very strong ability and accuracy of the prediction models.

However, the prediction ability of ANFIS model is slightly

better than regression model.

Figure 3. Effect of fabric width and weft count on compressed air

consumption.

Figure 4. Effect of reed count and loom speed on compressed air

consumption.

Table 6. Comparison of actual and predicted bursting strength values

No.Weft count,

Ne

Fabric

width, in

Loom speed,

RPM

Reed count,

dpi

Air cons.,

L/s

Regression model ANFIS model

Predicted

values

% Diff. from

actual

Predicted

values

% Diff. from

actual

1 10 63 550 28.75 14.8 14.88821 -0.596026 14.8 0.00

2 10 95 650 53.5 21.8 23.02616 -5.624584 21.7 -0.46

3 20 63 650 53.5 15 14.86274 -0.91507 15.1 0.67

4 20 95 450 53.5 18.6 20.19145 -8.556177 19 2.15

5 20 120 550 28.75 30.1 30.22865 -0.427422 30.2 0.33

6 40 95 550 53.5 19.3 20.68443 -7.173223 19.3 0.00

7 80 95 550 16 23.3 22.51655 -3.36247 22.5 -3.43

8 80 120 450 28.75 28.3 27.44887 -3.00752 28.5 0.71

Figure 5. Fitted line plot of actual and predicted air consumption

(a) by regression and (b) by ANFIS.

Predicting the Compressed Air Consumption in Air-Jet Weaving Fibers and Polymers 2014, Vol.15, No.2 395

Conclusion

Response surface and adaptive neuro-fuzzy inference

models were developed for predicting the compressed air

consumption in air jet weaving by taking weft yarn count,

fabric width, loom speed and reed count as predictor

variables. It was found that the compressed air consumption

is mainly influenced by the fabric width followed by loom

speed, reed count and weft yarn count respectively. It was

further found that both the response surface and ANFIS

models have the ability to predict compressed air consumption

with very good accuracy, with ANFIS model being slightly

better in performance. The developed model is being

successfully used in a weaving mill, and will be marketed in

other air jet weaving mills soon.

Acknowledgements

The authors would like to thank Mr. Zahid Rafique Mills

Manager Be Be Jan Colors Limited (Weaving Unit) for his

cooperation and support during experimentation.

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