Summary
One of the important production processes in the textile industry is the spinning process. Starting from cotton fibers, yarns are (usually) created on a rotor-spinning machine. The spinnability of a fiber is dependant on its quality and on the machine settings of the spinning machine. It would be a great benefit to be able to predict the spinnability and resulting strength of the yarn starting from a certain quality and from machine settings. To this end, two totally diiffferent modeling approaches can be considered: the so-called ‘white’ modeling and the so-called ‘black box’ modeling. In white modeling the process is described by mathematical equations, which are based upon (theoretical) physical knowledge of the process. Extensive physical information about the process is in this case available through physical, chemical or mechanical equations giving the user a thorough insight into the operation of the process. However, due to the large input (and output) dimensions of the fiber-to-yarn process and their complex interactions, no exact mathematical model of a spinning machine is known to exist nor is it likely that such a model will ever be constructed. A black box model, in contrast to white modeling, simply connects input parameters to the output without giving or containing any substantial physical information about the process itsellf Black box models have been successfully constructed by Pynckels et al. to predict the spinnability (Pynckels, 1995) and the characteristics (Pynckels, 1997) of the yarn using neural networks with a Backpropagation learning rule. Apart from the lack of physical information, these models also have no fault indication or measure of uncertainty about the results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonnarini A. (1997a), Anytime learning and adaptation of structured fuzzy behaviors, accepted for publication on the Special Issue of the Adaptive Behavior Journal about “Complete agent learning in complex environments”, Maja Mataric (Ed.), No. 5
Boullart L., Sette S. (1997), Genetic Algorithms: Theory & Applications, Journal A, Vol. 38, n.2, 13–23.
Boullart L., Sette S. (1998), High Performance Learning Classifier Systems, Engineering of Intelligent Systems (EIS’ 98), International ICSC Symposium, 11–13 February, Tenerife, 249–256.
BRITE/EURAM project BREU 00052-TT (1990–1993), ‘Research for a mathematical and rule based system which allows to optimize a cotton mixture, based on the interdependence of signifiicant fiiber properties, process parameters, yarn properties and spinning machinery performances.’
Darwin C. (1859), The Origin of Species: by Means of Natural Selection or the Preservation of Favored Races in the struggle for Life.
Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning., Addison Wesley Publishing Company.
Holland J.H. (1968), Hierarchical description of universal spaces and adaptive systems, Tech. Report ORA Projects 01252 and 0826, Ann Arbor, University of Michigan, Dept. Comp. Science & Comm. Science
Holland J.H. (1973), Genetic Algorithms and the Optimal Allocation of Trials., SIAM Journal of Computing, Vol. 2 (2), 88–105
Holland J.H., Reitman J.S. (1978), Cognitive systems based on adaptive algorithms. In D.A. Waterman & F. Hayes-Roth (Eds.), Pattern directed inference systems (pp.313–329). New York: Academic Press
Pynckels F., Sette S., Van Langenhove L., Kiekens P., Impe K. (1995), Use of Neural nets for Determining the Spinnability of Fibers, Journal of the Textile Institute, 86 (3), 425–437
Pynckels F., Kiekens P., Sette S., L. Van Langenhove, Impe K. Use of Neural Nets to Simulate the Spinning Process, The Journal of the Textile Institute, 88 (1), 440–447
Sette S., Boullart L., Van Langenhove L., Kiekens P. (1997a), Optimizing the Fiber-to-Yarn Production Process with a combined Neural Network/Genetic Algorithm Approach, Textile Research Journal, Vol. 67, No. 2, 84–92
Sette S., Learning systems by means of evolutionary algorithms, PhD thesis, Faculty of Applied Sciences, University Ghent, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sette, S., Van Langenhove, L. (2003). Generating a Rule Set for the Fiber-to-Yarn Production Process by Means of an Efficiency-based Classifier System. In: Sztandera, L.M., Pastore, C. (eds) Soft Computing in Textile Sciences. Studies in Fuzziness and Soft Computing, vol 108. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1750-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1750-8_6
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2516-9
Online ISBN: 978-3-7908-1750-8
eBook Packages: Springer Book Archive