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Performance Enhancement of Fabric Dyeing Process by Parametric Optimization

Kamal Ojha*, Rahul Sharma, Sunil Pathak

LANXESS IndiaPvt Ltd, Nagda (M.P) India

Abstract: Central composite design a response surface methodology approach is used for design the experiments, by fluctuating the accompanying three parameters namely fabric heating temperature, peroxide dosing time 1 and peroxide dosing time 2, at five levels to investigate their effects on the quality of the fabric i.e. fabric GSM and fabric width. Twenty experiments are planned using RSM and from the detailed examination of the results and testing of the varying input parameters for their significance at 95% confidence interval using ANOVA technique by “Design expert version 8 of stat ease Inc” were performed to calculate the percentage contribution of the input parameters and their interactions on the responses. From the results of the response surface model it is found that the optimized parametric combination for obtaining the required quality of fabric as per the requirement of MOL i.e. fabric GSM 150 and fabric width 180 cm; are very much achievable by using the identified parametric combinations namely fabric heating temperature as 90C, peroxide dosing time 1 as 5 minutes and peroxide dosing time 2 as 7.5 minutes.

Keywords: fabric quality, dyeing process, optimization, RSM, CCD

Received August 25, 2014; Revised August 30, 2014; Accepted September 01, 2014

Citation: Kamal Ojha, Rahul Sharma, Sunil Pathak, Performance Enhancement of Fabric Dyeing Process by Parametric Optimization. Journal of Mechanical Design and Vibration, 2 (4). 74-80. January 2014. doi:10.12691/jmdv-2-4-1

1. Introduction

Today is the world of most scientific and advanced level of dyeing. There are huge numbers of process to do coloration. Natural and man-made colours are also used. Single jersey knitted fabrics is generally used to make outerwear garments such as t-shirts. The knitted fabrics undergo a series of different processing treatments like scouring, bleaching (peroxide dosing), dyeing, softener padding and relax drying. These processes are carried out to impart a particular attribute related to that process like scouring for absorbency, bleaching for whiteness, dyeing to impart colour to fabric and finishing for improving softness and handle of the fabric. The properties of the knitted fabrics are influenced by various parameters like raw material, yarn structure, fabric structure, fabric heating temperature, peroxide dosing period, processing stages and finishing.

Many researchers have shown their interests in the development of various fabric dyeing processes. Saha et al. (2013) had done a comparative study on garments dyeing process and fabric dyeing process on various parameters. They compared the garments dyeing and fabric dyeing process by using direct dye and reactive dye and found that on changing pH in polyester fabric different types of shade are achieved. Use of reactive dye for dyeing the 100% cotton knit fabrics is done by Chowdhary et al. (2009). Their study was identified that precision setup of cycle time, run-time, dosing time and temperature gradient can improve meter-to-meter variation from 66%-86%; their study considers the factors namely water hardness, dyes combination and dyes leveling properties were optimum. The Effect of reactive dyeing and cellulase treatment on the pilling properties of cotton knitted fabric was studied by Kan and law (2012). Their study was aimed to study the relationship of reactive dyes and cellulase treatment on the pilling properties of cotton knitted fabric. Two commercial reactive dyes with different chemical structures were used. The dyeing and cellulase application were conducted in different. Experimental results revealed that the pilling results were varied depending on the different stages of applying reactive dyeing and cellulase treatment. One bath method dyeing of polyester/cotton blend fabric was experimentally explored by Najafi et al. (2009) during their research work their study was focused on improving the adhesion of chitin to the surface of polyester/cotton fibers; for this pre-treatment using NaOH solutions was performed. They reported that the colour strength of the dyed samples increased with the increased deposition of chitin on the fabric. In the most recent work Murugesh et al. (2013) reported the influence of wet processing on properties of single jersey knitted fabrics. The physical, dimensional and dyeing properties of the single jersey knitted fabrics were investigated after different wet processing stages and sequences. For this purpose, 2/40’s, 100%, organic cotton single jersey greige knitted fabric was developed and concluded that the dyeing behavior of Ready to stitch (RTS) fabric show that fabric dyed with mercerized ready for dyeing (RFD) fabric has greater colour strength compared to RTS fabric dyed with scoured RFD fabric. Syed et al. (2013) had done a comparative analysis of conventional and ultrasonic exhaust dyeing method for dyeing of organic cotton fabric. They reported dyeing with ultrasonic technique at 60C for 30 minutes gives colour strength value almost equal to the conventional recommended dyeing method. Further, they concluded that the dyeing of organic material using ultrasonic exhaust method saves energy and time. Tepparin et al. (2012) during their research work they studied and reported that the extraction of the natural dye from two different types of tamarind seed viz. sour-tamarind seed (TF) and sweet-tamarind seed (TO). The extracted dye solutions were prepared into a powder form and subsequently used for dyeing cotton and Bombyx Mori silk and Erisilk fabrics. From the experiment, the TF dye powder was easier soluble in water and could render a higher color strength than the TO dye powder. It was found for the tamarind-seed dyes that they could better exhausted on the silk fabrics than the cotton.

2. Details of Experimentation

In the present research work new outline of investigation using response surface methodology (RSM), central composite design technique is planned to investigate the influence of fabric heating and peroxide dosing time for improving the fabric width and fabric GSM. The desired value for fabric GSM is 150 GSM and fabric width is 180 cm.A Central Composite Design part of RSM was used to design the experimental run at five levels by changing the accompanying three parameters in particular, fabric heating (T), peroxide dosing time (PD1) and peroxide dosing time (PD2), while keeping the other affecting process parameters as constant in contrast with the past literatures. Table 1, Shows the levels of input parameters and fixed parameters. These values are chosen on the basis of past works and experimentations constraints.

The designs of experiments are designed with the help of a statistical software “Design expert 8” by Stat-Ease Inc. A rotatable central composite design from response surface methodology (RSM) is used for design the experimental run. Three main process parameters are chosen as input (namely Fabric heating temperature, Peroxide dosing time 1, Peroxide dosing time 2 while keeping the other process affecting parameters as constant due to machine restrictions. A lot of 1 kg of raw material (cotton fabric) is chosen as a material considering the machine feasibility. Each experimental run required 60 minutes to gives the selected output (namely Fabric width and Fabric GSM). The measurements for fabric width are taken with the precise and sophisticated measuring instruments by Mitutoya Japan, while a weighing machine is used to measure the GSM of the fabric, weighing of the fabric GSM is taken in an insulated environment to avoid errors. Random samples of fabric are chosen for measuring the fabric GSM from a lot.

Table 1. levels and values of input parameters and fixed parameters

3. Results and Discussion

In the present work new research outline of investigation is planned using rotatable a response surface methodology (RSM) approach, to investigate the influence of main parameters of dyeing machine with an objective to improve the fabric width (cm) and fabric GSM. The experiments are planned to study influence of input parameters at five levels by changing the accompanying three variables in particular, fabric heating temperature (0C), peroxide dosing time (minutes) and peroxide dosing time (minutes), while keeping the other affecting process parameters as constant in contrast with the past literatures.

4. Analysis of Fabric GSM

Table 2 illustrates the ANOVA table for fabric GSM and it can be seen that the Model F-value of 29.07 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" 29.07 will occur due to noise. In this case A, BC, A2, B2, C2 are found to be have significant effects on the responses. The percentage contribution (P%) are calculated for all the input parameters using Table 3 and it is evident that the calculated percentage contribution for Fabric GSM are fabric heating temperature is 16.76%, square term of fabric heating temperature is 39.61%, square term of peroxide dosing time 1 is 17.82% and square term of peroxide dosing time 2 is 42.20%. Thus this input parameters are found to have the highest influence on the Fabric GSM, and for future experimentation this parameters should be considered as most important and significant parameters, as the values of "Prob > F" are less than 0.0500.

The suggested theoretical mathematical model developed for fabric GSM using design expert software is presented in Equation 1.

[picture] | (1)

From the obtained experimental results in Table 2 and from Figure 1 it can be seen that the optimum parametric combination for obtaining the required fabric GSM are fabric heating temperature as 90C, peroxide dosing time 1 as 5 minutes and peroxide dosing time 2 as 7.5 minutes. As the obtained results shows that the 6 experiments are performed at these parametric combinations and the average values of fabric GSM obtained at these levels are 150.05 GSM which is very much real and marginally equivalent to the required fabric GSM i.e., 150 GSM. Further Figure 1 depicts the variation in fabric GSM with respect to the input parameters i.e., fabric heating temperature, peroxide dosing time 1 and peroxide dosing time 2. Figure 1 shows that at temperature below 90C and the average values of the fabric GSM are increasing rapidly while on at 90C the average values are found to be very significant as their values matches with the requirement of the industry, further on increasing the temperature beyond 90C the fabric GSM again increases i.e., the results obtained below 59 and beyond 90C are not acceptable with the industry requirements. From figure 1 it can be observed that the average values of the fabric GSM follows the same trends as with fabric heating temperature i.e., at value of peroxide dosing time 1 lesser than 5 minutes and higher than 5 minutes the Fabric GSM crumbles and not matching with the industry standards. Similar trends can be seen from figure 1 i.e., the fabric GSM quality diminishes at peroxide dosing time lesser and higher than 7.5 minutes. Further Figure 3 depicts the response surface plots for the interaction terms. However, in present case these interaction terms are not found to have significant effects on the quality of fabric GSM.

Table 2. Comprehensive results of all 20 experiments with the levels of input parameters

Table 3. ANOVA results for Fabric GSM

Figure 1. Effects of variable input parameters on Fabric GSM:(a) effects of fabric heating temperature on GSM; (b) ) effects of peroxide dosing time 1 on GSM (c) effects of peroxide dosing time 2 on GSM

Figure 2. Interaction Effects of variable input parameters on Fabric GSM: (a) effects of fabric heating temperature and peroxide dosing time 1 on GSM (b) effects of fabric heating temperature and peroxide dosing time 2 on GSM (c) effects of peroxide dosing time 1 and peroxide dosing time 2 on GSM

The 3D surface graphs for fabric GSM are shown in Figure 2, have curvilinear profile in accordance to the quadratic model fitted.

5. Analysis on Fabric Width

Table 4 presents the ANOVA table for Fabric width, and it is clear from the observed values that the “Model F-value” of 15.37 implies the model is significant for fabric width. There is only a 0.01% chance that a "Model F-Value” 15.37 will occur due to noise. In this case A, A², B², C² are significant model terms. The percentage contribution (P%) are calculated for all the input parameters using Table 4 and it is evident that the calculated percentage contribution for Fabric width are fabric heating temperature is 17.5%, square term of fabric heating temperature is 50.65%, square term of peroxide dosing time 1 is 10.48% and square term of peroxide dosing time 2 is 35.98% and. Thus this input parameters are found to have the highest influence on the Fabric GSM, and for future experimentation this parameters should be considered as most important and significant parameters. However for this research work the main focus is on to find the fabric width and fabric GSM for this the interaction of each parameters are necessary to be examine with the response.

Table 4. ANOVA results for Fabric Width

Figure 3. Effects of variable input parameters on Fabric width: (a) effects of fabric heating temperature on width; (b) ) effects of peroxide dosing time 1 on width (c) effects of peroxide dosing time 2 on width

ANOVA results for fabric GSM and fabric width using statistical software is presented in Eqn.2

[picture] | (2)

On detailed examinations for the results evaluated for all 20 experiments from Table 2. It can be observed from the obtained results that the 6 experiments are performed at parametric combination for obtaining the required fabric width is fabric heating temperature as 90C, peroxide dosing time 1 as 5 minutes and peroxide dosing time 2 as 7.5 minutes. From the obtained result it shows that these parametric combinations the average values of fabric width are 180.28 cm which is very real and near to the industry required value of fabric width i.e. 180 cm. The averages of experimental and theoretical values for fabric width are calculated to study their variation at varying input parameters. Figure 2 depicts the variation of fabric width with input parameters i.e., fabric heating temperature, peroxide dosing time 1 and peroxide dosing time 2.

Figure 3 shows that the fabric width is increasing with increase in temperature and reaches its maximum value 180.28 cm at 90C, and on heating more than 90C the fabric width goes on opposite side i.e. its value decreases with increase in temperature above 90C. Figure 2 depicts the variation of width with the peroxide dosing time 1, as from the ANOVA results we can see the square term of peroxide dosing time 1 are very significant but it is not the playing the major role on quality of fabric width as the parentage contribution shows the influence of peroxide dosing time 1is only 10.5%, due to which the trends obtained for the this parameter are non-uniform and it can be seen from the graph that required value for fabric width is obtained when introducing the peroxide for 5 minutes. Further from figure 2 it can be seen that while dosing the peroxide after the four intermediate processes especially “Aquachron” takes longer time as compared with the peroxide dosing time 1.

Figure 4 depicts the 2D surface graphs for fabric width justified that at center values the required values are achievable. These curves have curvilinear profile as the model fit arequadratic.

Figure 4. Interaction Effects of variable input parameters on Fabric width: (a) effects of fabric heating temperature and peroxide dosing time 1 on width (b) effects of fabric heating temperature and peroxide dosing time 2 on width (c) effects of peroxide dosing time 1 and peroxide dosing time 2 on width

6. Conclusion

The aim of the present investigation was to find the optimum parametric combinations for improving the fabric quality in a dyeing process. A rotatable central composite design from response surface methodology (RSM) is used for design the experiment. Three main process parameters are chosen as input (namely Fabric heating temperature, Peroxide dosing time 1, Peroxide dosing time 2. The obtained results and various interactions suggest the following conclusion points:

1. Fabric heating temperature, peroxide dosing time 1 and peroxide dosing time 2 are having the major influence on the quality of fabric GSM and fabric width.

2. On detailed observation of results and interaction it can be concluded that the optimum parametric combinations for achieving the required quality of fabric i.e. Fabric weight 150 GSM and fabric width 180 cm is very much reachable at the identified parametric combinations i.e. Fabric heating temperature value equals to 90C, Peroxide dosing 1 for 5 minutes and peroxide dosing 2 for 7.5 minutes.

3. From the experimentation and results it is evident that the quality of the fabric is enhanced significantly by using the identified parametric combination. Further I is suggested and consulted to the manager of the industry to use the above mentioned parametric combination for the future production to increase the productivity and efficiency of the plant.

4. As the required quality is now accessible, it will help the industry to reduce the rejection rate and ultimately the reducing the production cost.

5. From the results it was observed that the time taken for peroxide dosing 2 is reduced by 2.5 minutes which in returns saves the process time.

6. Reducing the defects during the manufacturing this improving the corporate image of the industries.

7. Improvement in fabric quality will enhance the customer satisfaction.

Acknowledgement

Author is highly acknowledged towards the Maral Overseas Ltd, Khalghat, Khargone (M.P) India for allowing us to use their facility of Fabric Dyeing Setup at their plant. The support staff at MOL are very helpful and supportive especially Mr. A K Rai, AGM (Operations and Mechanical maintenance department) at MOL.

References

[1]   | Murugesh B. K. and Selvadass. M (2013) “Influence of Wet Processing on Properties of Single Jersey Knitted Fabrics” International Journal of Fiber and Textile Research 2013; 3(1): 18-30, ISSN 2277-7156.
[2]   | Najafi et al. (2009) “One bath method dyeing of polyester/cotton blend fabric with sulphatoethylsulphonyl disperse/reactive dyes treatment by chitin biopolymer” African Journal of Biotechnology Vol. 8 (6), pp. 1127-1135.
[3]   | Syed et al. (2013) “Dyeing of Organic Cotton Fabric using Conventional and Ultrasonic Exhaust Dyeing Method” Mehran University Research Journal of Engineering & Technology, Volume 32, No. 2, April, 2013, ISSN 0254-7821.
[4]   | Saha et al. (2013) “Comparative study on Garments dyeing process and Fabric dyeing process on various parameters (PH, M: L, softener etc)” International Journal of Modern Engineering Research (IJMER), Vol. 3, Issue. 4, Jul - Aug. 2013, ISSN: 2249-6645, pp-2434-2441.
[5]   | Tepparin et al. (2012) “Dyeing of Cotton, Bombyx Mori and Eri Silk Fabrics with the Natural Dye Extracted from Tamarind Seed” International Journal of Bioscience, Biochemistry and Bioinformatics, Vol. 2, No. 3, May 2012.
[6]   | Chowdhury et al. (2009) “Process for Level Dyeing of 100% Cotton knit Fabrics with reactive Dye” j. innov. dev. strategy. 3 (4): 1-8.
[7]   | Kan and Law (2012) “Effect of Reactive Dyeing and cellulase Treatment on the pilling properties of Cotton Knitted Fabric” International Conference: Textiles & Fashion 2012, Bangkok Thailand.