Reducing Variation of Dyeing Process in Textile Manufacturing Industry
This study deals with a multi-criteria optimization
problem which has been transformed into a single objective
optimization problem using Response Surface Methodology (RSM),
Artificial Neural Network (ANN) and Grey Relational Analyses
(GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques
which can be used for solving multi-criteria optimization problem.
There have been two main purposes of this research as follows.
1. To determine optimum and robust fiber dyeing process
conditions by using RSM and ANN based on GRA,
2. To obtain the best suitable model by comparing models
developed by different methodologies.
The design variables for fiber dyeing process in textile are
temperature, time, softener, anti-static, material quantity, pH,
retarder, and dispergator. The quality characteristics to be evaluated
are nominal color consistency of fiber, maximum strength of fiber,
minimum color of dyeing solution. GRA-RSM with exact level
value, GRA-RSM with interval level value and GRA-ANN models
were compared based on GRA output value and MSE (Mean Square
Error) performance measurement of outputs with each other. As a
result, GRA-ANN with interval value model seems to be suitable
reducing the variation of dyeing process for GRA output value of the
model.
[1] C.C. Kuo and S. Pietras, "Applying regression analysis to improve
dyeing process quality: a case study", International Journal Advance
Manufacturing Technologies, vol.49, pp. 357-368, 2010.
[2] K. Ravikumar, S. Krishnan, S. Ramalingam, and K. Balu, "Optimization
of process variables by the application of response surface methodology
to optimize the process variables for Reactive Red and Acid Brown dye
removal using a novel adsorbent", Dyes and Pigments, vol.72, pp. 66-
74, 2006.
[3] C.-F. J. Kuo, C.-D. Chang, T.-L. Su, and C.-T. Fu, "Optimization of the
Dyeing Process and Prediction of Quality Characteristics on Elastic
Fiber Blending Fabrics", Polymer-Plastics Technology and Engineering,
vol. 47, no.7, pp. 678 - 687, 2008.
[4] K.W. Hench and A.M. Al-Ghanim, "The application of a neural network
methodology to the analysis of a dyeing operation" in ANNIE ÔÇÿ95:
Artificial neural networks in engineering, St. Louis, MO (United States),
12-15 Nov, 1995, pp 873-878.
[5] G. Köksal, "Robust Design of Batch Dyeing Process", Degree of Doctor
of Philosophy, Department of Industrial Engineering, Graduate Faculty
of North Carolina State University, Raleigh, NC, 1992.
[6] C. F. J. Kuo and C. C. Fang, "Optimization of the Processing Conditions
and Prediction of the Quality for Dyeing Nylon and Lycra Blended
Fabrics", Fibers and Polymers, vol.4, pp.344-351, 2006.
[7] M. I. Jahmeerbacus, N. Kistamah and R. B. Ramgulam, "Fuzzy control
of dye bath pH in exhaust dyeing", Coloration technology, vol.120,
pp.51-55, 2004.
[8] M.M. Kamel, H.M. Helmy, H.M. Mashaly and H.H. Kafafy, "Ultrasonic
assisted dyeing: Dyeing of acrylic fabrics C.I. Astrazon Basic Red 5BL
200%", Ultrasonics Sonochemistry, vol.17, pp. 92-97, 2010.
[9] H. H. Wang, "Accessibility of Acrylic Fiber to Basic Dye", Journal of
Applied Polymer Science, vol.111, pp.189-193, 2009.
[10] C. Yu and Y. Chen, "Study on Dyeing Properties of Functional Acrylic
Fiber", Journal of Macromolecular Science, Part A: Pure and Applied
Chemistry, vol.43, no.11, pp.1695-1702, 2006.
[11] A. Rangone, "An analytical hierarchy process framework for comparing
the overall performance of manufacturing departments", International
Journal of Operations & Production Management, vol.16, no.8, pp.104 -
119, 1996.
[12] R. Ramanathan and L.S. Ganesh, "Group Preference Aggregation
Methods Employed in AHP: An Evaluation and Intrinsic Process for
Deriving Members' Weightages", European Journal of Operational
Research, vol.79, pp.249-265, 1994.
[13] T.L. Saaty, The Analytic Hierarchy Proces, New York, McGraw-Hill
International, 1980
[14] J.L. Deng, "Introduction to Grey system theory", J. Grey System, vol.1,
pp.1-24, 1989.
[15] M. H. I. Ibrahim, N. Muhamad, A. B. Sulong, N. H. M. Nor, K. R.
Jamaludin, M. R. Harun, and Murtadhahadi, "Optimization of Micro
Metal Injection Molding with Multiple Performance Characteristics
using Grey Relational Grade", in International Conference On Advances
In Materials And Processing Technologies, January 17,
2011,vol.1315,pp.713-718.
[16] G. E. P. Box, and K.B. Wilson, "On the Experimental Attainment of
Optimum Conditions (with discussion)", Journal of the Royal Statistical
Society Series B, vol.13, no.1, pp.1-45, 1951.
[17] N. Bradley, The Response Surface Methodology, Department of
Mathematical Sciences, Indiana University of South Bend, 2007.
[18] D.Bas and I. H. Boyac─▒, "Modeling and optimization I: Usability of
response surface methodology", Journal of Food Engineering, vol.78,
pp.836-845, 2007.
[19] Y.-N. Chang, J.-C. Huang, C.-C. Lee, I.-L. Shih and Y.-M. Tzeng, "Use
of response surface methodology to optimize culture medium for
production of lovastatin by Monascus ruber", Enzyme and Microbial
Technology, vol.30, pp.889-894, 2002.
[20] M. A. Bezerra, R. E.Santelli , E. P. Oliveira, L. S. Villar and L.A.
Escaleira, "Response surface methodology (RSM) as a tool for
optimization in analytical chemistry", Talanta ,vol.76, pp. 965-977,
2008.
[21] J. Li, C. Ma, Y. Ma, Y. Li, W. Zhou and P. Xu, "Medium optimization
by combination of response surface methodology and desirability
function: an application in glutamine production", Applied Microbiology
and Biotechnology, vol.74, no.3, pp.563-571, 2007.
[22] J. Yao, N. Teng, H. Poh and C.L.Tan, "Forecasting and Analysis of
Marketing Data Using Neural Networks", Journal of Information
Science and Engineering, vol. 14, pp.843-862, 1998.
[23] A.H. Sung, "Ranking importance of input parameters of neural
networks", Expert Systems with Applications, vol.15, pp. 405-411, 1998
[24] J. D. Olden, M. K. Joy, R. G. Death, "An accurate comparison of
methods for quantifying variable importance in artificial neural networks
using simulated data", Ecological Modelling, vol.178, pp.389-397,
2008.
[25] StatSoft, Inc. Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB:
http://www.statsoft.com/textbook/ ,2011.
[1] C.C. Kuo and S. Pietras, "Applying regression analysis to improve
dyeing process quality: a case study", International Journal Advance
Manufacturing Technologies, vol.49, pp. 357-368, 2010.
[2] K. Ravikumar, S. Krishnan, S. Ramalingam, and K. Balu, "Optimization
of process variables by the application of response surface methodology
to optimize the process variables for Reactive Red and Acid Brown dye
removal using a novel adsorbent", Dyes and Pigments, vol.72, pp. 66-
74, 2006.
[3] C.-F. J. Kuo, C.-D. Chang, T.-L. Su, and C.-T. Fu, "Optimization of the
Dyeing Process and Prediction of Quality Characteristics on Elastic
Fiber Blending Fabrics", Polymer-Plastics Technology and Engineering,
vol. 47, no.7, pp. 678 - 687, 2008.
[4] K.W. Hench and A.M. Al-Ghanim, "The application of a neural network
methodology to the analysis of a dyeing operation" in ANNIE ÔÇÿ95:
Artificial neural networks in engineering, St. Louis, MO (United States),
12-15 Nov, 1995, pp 873-878.
[5] G. Köksal, "Robust Design of Batch Dyeing Process", Degree of Doctor
of Philosophy, Department of Industrial Engineering, Graduate Faculty
of North Carolina State University, Raleigh, NC, 1992.
[6] C. F. J. Kuo and C. C. Fang, "Optimization of the Processing Conditions
and Prediction of the Quality for Dyeing Nylon and Lycra Blended
Fabrics", Fibers and Polymers, vol.4, pp.344-351, 2006.
[7] M. I. Jahmeerbacus, N. Kistamah and R. B. Ramgulam, "Fuzzy control
of dye bath pH in exhaust dyeing", Coloration technology, vol.120,
pp.51-55, 2004.
[8] M.M. Kamel, H.M. Helmy, H.M. Mashaly and H.H. Kafafy, "Ultrasonic
assisted dyeing: Dyeing of acrylic fabrics C.I. Astrazon Basic Red 5BL
200%", Ultrasonics Sonochemistry, vol.17, pp. 92-97, 2010.
[9] H. H. Wang, "Accessibility of Acrylic Fiber to Basic Dye", Journal of
Applied Polymer Science, vol.111, pp.189-193, 2009.
[10] C. Yu and Y. Chen, "Study on Dyeing Properties of Functional Acrylic
Fiber", Journal of Macromolecular Science, Part A: Pure and Applied
Chemistry, vol.43, no.11, pp.1695-1702, 2006.
[11] A. Rangone, "An analytical hierarchy process framework for comparing
the overall performance of manufacturing departments", International
Journal of Operations & Production Management, vol.16, no.8, pp.104 -
119, 1996.
[12] R. Ramanathan and L.S. Ganesh, "Group Preference Aggregation
Methods Employed in AHP: An Evaluation and Intrinsic Process for
Deriving Members' Weightages", European Journal of Operational
Research, vol.79, pp.249-265, 1994.
[13] T.L. Saaty, The Analytic Hierarchy Proces, New York, McGraw-Hill
International, 1980
[14] J.L. Deng, "Introduction to Grey system theory", J. Grey System, vol.1,
pp.1-24, 1989.
[15] M. H. I. Ibrahim, N. Muhamad, A. B. Sulong, N. H. M. Nor, K. R.
Jamaludin, M. R. Harun, and Murtadhahadi, "Optimization of Micro
Metal Injection Molding with Multiple Performance Characteristics
using Grey Relational Grade", in International Conference On Advances
In Materials And Processing Technologies, January 17,
2011,vol.1315,pp.713-718.
[16] G. E. P. Box, and K.B. Wilson, "On the Experimental Attainment of
Optimum Conditions (with discussion)", Journal of the Royal Statistical
Society Series B, vol.13, no.1, pp.1-45, 1951.
[17] N. Bradley, The Response Surface Methodology, Department of
Mathematical Sciences, Indiana University of South Bend, 2007.
[18] D.Bas and I. H. Boyac─▒, "Modeling and optimization I: Usability of
response surface methodology", Journal of Food Engineering, vol.78,
pp.836-845, 2007.
[19] Y.-N. Chang, J.-C. Huang, C.-C. Lee, I.-L. Shih and Y.-M. Tzeng, "Use
of response surface methodology to optimize culture medium for
production of lovastatin by Monascus ruber", Enzyme and Microbial
Technology, vol.30, pp.889-894, 2002.
[20] M. A. Bezerra, R. E.Santelli , E. P. Oliveira, L. S. Villar and L.A.
Escaleira, "Response surface methodology (RSM) as a tool for
optimization in analytical chemistry", Talanta ,vol.76, pp. 965-977,
2008.
[21] J. Li, C. Ma, Y. Ma, Y. Li, W. Zhou and P. Xu, "Medium optimization
by combination of response surface methodology and desirability
function: an application in glutamine production", Applied Microbiology
and Biotechnology, vol.74, no.3, pp.563-571, 2007.
[22] J. Yao, N. Teng, H. Poh and C.L.Tan, "Forecasting and Analysis of
Marketing Data Using Neural Networks", Journal of Information
Science and Engineering, vol. 14, pp.843-862, 1998.
[23] A.H. Sung, "Ranking importance of input parameters of neural
networks", Expert Systems with Applications, vol.15, pp. 405-411, 1998
[24] J. D. Olden, M. K. Joy, R. G. Death, "An accurate comparison of
methods for quantifying variable importance in artificial neural networks
using simulated data", Ecological Modelling, vol.178, pp.389-397,
2008.
[25] StatSoft, Inc. Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB:
http://www.statsoft.com/textbook/ ,2011.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62166", author = "M. Zeydan and G. Toğa", title = "Reducing Variation of Dyeing Process in Textile Manufacturing Industry", abstract = "This study deals with a multi-criteria optimization
problem which has been transformed into a single objective
optimization problem using Response Surface Methodology (RSM),
Artificial Neural Network (ANN) and Grey Relational Analyses
(GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques
which can be used for solving multi-criteria optimization problem.
There have been two main purposes of this research as follows.
1. To determine optimum and robust fiber dyeing process
conditions by using RSM and ANN based on GRA,
2. To obtain the best suitable model by comparing models
developed by different methodologies.
The design variables for fiber dyeing process in textile are
temperature, time, softener, anti-static, material quantity, pH,
retarder, and dispergator. The quality characteristics to be evaluated
are nominal color consistency of fiber, maximum strength of fiber,
minimum color of dyeing solution. GRA-RSM with exact level
value, GRA-RSM with interval level value and GRA-ANN models
were compared based on GRA output value and MSE (Mean Square
Error) performance measurement of outputs with each other. As a
result, GRA-ANN with interval value model seems to be suitable
reducing the variation of dyeing process for GRA output value of the
model.", keywords = "Artificial Neural Network, Grey Relational Analysis,Optimization, Response Surface Methodology", volume = "5", number = "11", pages = "2471-9", }
