Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural
The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
[1] Llonch, M. “La Competitividad de Los Distritos Catalanes del Género
de Punto (1961-2004)”. Monografías de la Revista de Historia Industrial.
Publicions de la Universitat de Barcelona, pp. 1-27, 2004.
[2] Capdevila, X. “Regulación de la Tricotosa Rectilínea y su Influencia
Sobre la Longitud de Malla”, Boletín Intertex (U.P.C), vol.121, pp. 23-
29, 2002.
[3] Barretto, S. “Fabricación de Prendas en Tejido de Punto”, FADU UBA.
Retrieved August 13, 2014, from http://cursos.fadu.uba.ar/apuntes/
Indumentaria%20I/unidad%20practica%20n%20%201/7-
%20Fabricacion%20de%20prendas%20en%20tejido%20de%20punto.p
df.
[4] Pocoroba, R. “Análisis de los factores que determinan la formación del
pilling en tejido de punto”, Tesis inédita Maestro en Ciencias, Instituto
Politécnico Nacional. 2006
[5] Henning, H. “Tipos de encogimiento de los géneros de punto de lana y
su medida”, Conferencias Escuela Técnica Superior de Ingenieros
Industriales de Terrasa, pp. 71-86, 1969.
[6] Lien, H. Lee, S. “A Method of Feature Selection for Textile Yarn
Grading Using the Effective Distance between Clusters”, Text Res J,
Vol. 72, no. 10, pp. 870-878, 2002.
[7] Pynckels, F, Kiekens, P. Sette, S. Van Langgenhove, L. Impe, K. “The
Use of Neural Nets to Simulate the Spinning Process”. J Text Inst, vol.
88, no. 4, pp. 440-448, 1997.
[8] Park, C. Kang, T. “Objective Rating of Seam Pucker Using Neural
Networks”, Text Res J, Vol. 67, no. 7, pp. 497-502, 1997.
[9] Ludwig, L. Sapozhnikova, E. Lunin, V. Rosenstiel, W. “Error
Classification and Yield Prediction of Chips in Semiconductor Industry
Applications”, Neur Comput App, Vol. 9, pp. 202–210, 2000.
[10] Verikas, A. Malmqvist, K. Bergman, L. Signahl, M. “Colour
Classification by Neural Networks in Graphic Arts”, Neur Comput App,
Vol. 7, pp. 52–64, 1998.
[11] Fazlollahtabar, H. Mahdavi-Amiri, N. “Design of a Neuro-Fuzzy–
Regression Expert System to Estimate Cost in a Flexible Jobshop
Automated Manufacturing System”, Int J Adv Manuf Technol, Vol. 67,
pp. 1809–1823, 2013.
[12] Majumdar, A. Ghosh, A. “Yarn Strength Modelling Using Fuzzy Expert
System”, Journal of Engineered Fibers and Fabrics Vol. 3, no. 4, pp. 61-
68, 2008.
[13] Hsin, L. Shyong, L. “Applications of Neural Networks for Grading
Textile Yarns”, Neural Comput & Applic, Vol. 13, pp. 185–192, 2004.
[14] Baeza, R. and Cedillo, G. “Statistical Model of the Knitting System
Dynamics”, Proceedings of the 15th Annual International Conference on
Industrial Engineering Theory, Applications and Practice. México City,
2010.
[15] Zurada, M. “Introduction to Artificial Neural Systems”, West Publishing
Company, New York, 1992.
[1] Llonch, M. “La Competitividad de Los Distritos Catalanes del Género
de Punto (1961-2004)”. Monografías de la Revista de Historia Industrial.
Publicions de la Universitat de Barcelona, pp. 1-27, 2004.
[2] Capdevila, X. “Regulación de la Tricotosa Rectilínea y su Influencia
Sobre la Longitud de Malla”, Boletín Intertex (U.P.C), vol.121, pp. 23-
29, 2002.
[3] Barretto, S. “Fabricación de Prendas en Tejido de Punto”, FADU UBA.
Retrieved August 13, 2014, from http://cursos.fadu.uba.ar/apuntes/
Indumentaria%20I/unidad%20practica%20n%20%201/7-
%20Fabricacion%20de%20prendas%20en%20tejido%20de%20punto.p
df.
[4] Pocoroba, R. “Análisis de los factores que determinan la formación del
pilling en tejido de punto”, Tesis inédita Maestro en Ciencias, Instituto
Politécnico Nacional. 2006
[5] Henning, H. “Tipos de encogimiento de los géneros de punto de lana y
su medida”, Conferencias Escuela Técnica Superior de Ingenieros
Industriales de Terrasa, pp. 71-86, 1969.
[6] Lien, H. Lee, S. “A Method of Feature Selection for Textile Yarn
Grading Using the Effective Distance between Clusters”, Text Res J,
Vol. 72, no. 10, pp. 870-878, 2002.
[7] Pynckels, F, Kiekens, P. Sette, S. Van Langgenhove, L. Impe, K. “The
Use of Neural Nets to Simulate the Spinning Process”. J Text Inst, vol.
88, no. 4, pp. 440-448, 1997.
[8] Park, C. Kang, T. “Objective Rating of Seam Pucker Using Neural
Networks”, Text Res J, Vol. 67, no. 7, pp. 497-502, 1997.
[9] Ludwig, L. Sapozhnikova, E. Lunin, V. Rosenstiel, W. “Error
Classification and Yield Prediction of Chips in Semiconductor Industry
Applications”, Neur Comput App, Vol. 9, pp. 202–210, 2000.
[10] Verikas, A. Malmqvist, K. Bergman, L. Signahl, M. “Colour
Classification by Neural Networks in Graphic Arts”, Neur Comput App,
Vol. 7, pp. 52–64, 1998.
[11] Fazlollahtabar, H. Mahdavi-Amiri, N. “Design of a Neuro-Fuzzy–
Regression Expert System to Estimate Cost in a Flexible Jobshop
Automated Manufacturing System”, Int J Adv Manuf Technol, Vol. 67,
pp. 1809–1823, 2013.
[12] Majumdar, A. Ghosh, A. “Yarn Strength Modelling Using Fuzzy Expert
System”, Journal of Engineered Fibers and Fabrics Vol. 3, no. 4, pp. 61-
68, 2008.
[13] Hsin, L. Shyong, L. “Applications of Neural Networks for Grading
Textile Yarns”, Neural Comput & Applic, Vol. 13, pp. 185–192, 2004.
[14] Baeza, R. and Cedillo, G. “Statistical Model of the Knitting System
Dynamics”, Proceedings of the 15th Annual International Conference on
Industrial Engineering Theory, Applications and Practice. México City,
2010.
[15] Zurada, M. “Introduction to Artificial Neural Systems”, West Publishing
Company, New York, 1992.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:71115", author = "Baeza S. Roberto", title = "Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural", abstract = "The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.", keywords = "Neural network, dry relaxation, knitting, linear
regression.", volume = "9", number = "9", pages = "1160-6", }