Prediction of Polypropylene Yarn Shrinkage in the Heat-Setting Process Using the Fuzzy Inference System

Dadgar, Mehran

doi:10.5604/01.3001.0014.3146

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Tytuł artykułu

Prediction of Polypropylene Yarn Shrinkage in the Heat-Setting Process Using the Fuzzy Inference System

Autorzy

Dadgar Mehran

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DOI

10.5604/01.3001.0014.3146

Warianty tytułu

Przewidywanie skurczu przędzy polipropylenowej w procesie utrwalania termicznego z wykorzystaniem modelu logiki rozmytej

Języki publikacji

Abstrakty

In the carpet industry, yarn shrinkage is a very important specification, the percent of which being affected by heat-setting parameters, time and temperature. In order to obtain the best uniform appearance of carpets, the shrinkage of pile yarns should be minimum in the carpet sizing process. Inappropriately heat-set yarn may cause undesirable shrinkage and uneven pile height on carpets after the sizing process. It could be useful for manufacturers to understand the optimum condition of heat setting to obtain the low shrinkage of heat-set yarns before weaving. Therefore, a fuzzy logic model was designed to predict the shrinkage percentage of polypropylene yarn in different heat-setting conditions. Time and temperature are taken into account as input variables, and yarn shrinkage is predicted as the output. For validation of the model, yarn samples were heat set over various periods of time, at different temperatures, and finally yarn shrinkages were measured experimentally. The results of the fuzzy model prediction compared to regression results show that the fuzzy results present a good and better match with experimental results, with an acceptable R² = 0.97 and average error (2.59%).

W przemyśle dywanowym skurcz przędzy jest bardzo ważną specyfikacją, na procent której mają wpływ parametry utrwalania termicznego, czas i temperatura. Aby uzyskać jak najlepszy jednolity wygląd dywanów, skurcz przędzy runowej w procesie klejenia powinien być minimalny. Nieprawidłowo utrwalona na gorąco przędza może powodować niepożądany skurcz i nierówną wysokość runa na dywanach po zaklejaniu. Dla producentów może być przydatne zrozumienie optymalnych warunków stabilizacji termicznej w celu uzyskania niskiego skurczu przędz utrwalanych termicznie przed tkaniem. Dlatego w pracy zaprojektowano model logiki rozmytej do przewidywania procentowego skurczu przędzy polipropylenowej w różnych warunkach utrwalania termicznego. Czas i temperatura są brane pod uwagę jako zmienne wejściowe, a skurcz przędzy jest przewidywany jako wynik. W celu walidacji modelu próbki przędzy utrwalano termicznie w różnych okresach czasu, w różnych temperaturach, a na koniec zmierzono eksperymentalnie skurcz przędzy. Wyniki predykcji modelu rozmytego w porównaniu z wynikami regresji pokazują, że wyniki rozmyte dobrze korelują z wynikami eksperymentalnymi, przy akceptowalnym R² = 0,97 i średnim błędzie (2,59%).

Słowa kluczowe

heat setting shrinkage fuzzy inference system polypropylene

utrwalanie termiczne skurcz system logiki rozmytej polipropylen

Wydawca

Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny

Czasopismo

Fibres & Textiles in Eastern Europe

Rocznik

2020

Tom

Vol. 28, no. 6 (144)

Strony

35--41

Opis fizyczny

Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Dadgar Mehran

M_dadgar@neyshabur.ac.ir

University of Neyshabur, Department of Textile, Iran

Bibliografia

1. Rudolf A Geršak J, Smole MS. The Effect of Heat Treatment Conditions using the Drawing Process on the Properties of PET Filament Sewing Thread. Textile Research Journal 2012; 82(2): 161-171.
2. Shen FL, et al. Effect of Heat-Setting Temperature on the Structure and Performance of Ultra-Fine Denier PET Full Drawing Yarn. Advanced Materials Research 2011; 197: 1276-1280.
3. Whittle M, Orofino T, Miller K. Relationship between Mechanical Properties of Carpet and comfort in Walking. Journal of Biomechanics 1993; 26(3): 323.
4. Statton W, Hearle J, Miles L. The Setting of Fibres and Fabrics. Merrow Publishing Co. Ltd Herts, England 1971.
5. Horrocks AR, Anand S, Anand SC. Handbook of Technical Textiles. Vol. 12. Crc Press 2000.
6. Crawshaw GH. Carpet Manufacture. Vol. 2. Wronz Developments Christchurch 2002.
7. Kish MH, Shoushtari SA, Kazemi S. Effects of Cold-Drawing and Heat-Setting on the Structure and Properties of Medium Speed Spun Polypropylene Filaments. Iranian Polymer Journal 2000; 9: 239-248.
8. Simal AL, Martin AR. Structure of Heat-Treated Nylon 6 and 6.6 Fibers. I. The Shrinkage Mechanism. Journal of Applied Polymer Science 1998; 68(3): 441-452.
9. Gao S-Y, et al. The Influences of Heat Treatment on the Shrinkage and Tensile Property of One-Step Process POY/FDY Polyester Combined Yarn. Donghua Daxue Xuebao (Ziran Ban), 2011; 37(3): 267-271.
10. Hui C, et al. Characterization of High Modulus and Low Shrinkage Polyester Technical Yarn by Simulation Curing Method. Synthetic Fiber in China, 2011. 11: p. 9.
11. Cardamone JM, Yao J, Phillips JG, Combined Bleaching, Shrinkage Prevention, and Biopolishing of Wool Fabrics. Textile Research Journal 2005; 75(2): 169-174.
12. Ma Y, Tan M, Wu K. Effect of Different Geometric Polypropylene Fibers on Plastic Shrinkage Cracking of Cement Mortars. Materials and Structures 2002; 35(3): 165-169.
13. Pelisser F, et al. Effect of the Addition of Synthetic Fibers to Concrete Thin Slabs on Plastic Shrinkage Cracking. Construction and Building Materials 2010; 24(11): 2171-2176.
14. Samui BK, et al. Hysteresis Characteristics of High Modulus Low Shrinkage Polyester Tire Yarn and Cord. Rubber Chemistry and Technology 2011; 84(4): 565-579.
15. Headinger MH, Rudisill EN, Luckey DW. Low Shrinkage, Dyeable MPD-I Yarn, 2011, Google Patents.
16. Lin J-J. Prediction of Yarn Shrinkage Using Neural Nets. Textile Research Journal 2007; 77(5): p. 336-342.
17. Çeven EK, Ozdemir O. Using Fuzzy Logic to Evaluate and Predicte Chenille Yarn’s Shrinkage Behaviour. FIBRES & TEXTILES in Eastern Europe 2007; 15, 3(62): 55-59.
18. Everaert V, Vanneste M, Ruys L. Techniques for the Evaluation of Fiber Heat Setting in PP and PA Carpet Yarns, Unitex, 1999.
19. Gupta V, Kumar S. The Effect of Heat Setting on the Structure and Mechanical Properties of Poly (Ethylene Terephthalate) Fiber. II. The Elastic Modulus and Its Dependence on Structure. Journal of Applied Polymer Science 1981; 26(6): 1877-1884.
20. Gupta A, Maiti A. Effect of Heat Treatment on the Structure and Mechanical Properties of Polyacrylonitrile Fibers. Journal of Applied Polymer Science 1982, 27(7): p. 2409-2416.
21. Sardag S, Ozdemir O, Kara I. The Effects of Heat-Setting on the Properties of Polyester/Viscose Blended Yarns. FIBRES & TEXTILES in Eastern Europe, 2007; 15, 4(63): 50-53.
22. Sarkeshick S, et al. An Investigation on the Effects of Heat-Setting Process on the Properties of Polypropylene Bulked Continuous Filament Yarns. The Journal of The Textile Institute 2009; 100(2): 128-134.
23. Samuels RJ. Structured Polymer Properties: The Identification, Interpretation, and Application of Crystalline Polymer Structure, Wiley New York 1974.
24. Majumdar A. Modeling of Cotton Yarn Hairiness using Adaptive Neuro-Fuzzy Inference System. Indian Journal of Fibre & Textile Research 2010; 35(2): 121.
25. Ross TJ. Fuzzy Logic with Engineering Applications, John Wiley & Sons 2009.
26. Kayacan M C, Dayik M, Colak O, Kodaloglu M. Velocity Control of Weft Insertion on Air Jet Looms by Fuzzy Logic. FIBRES & TEXTILES in Eastern Europe 2004; 12, 3(47): 29-33.
27. Kor M, et al. Modeling and Optimization of High Chromium Alloy Wear in Phosphate Laboratory Grinding Mill with Fuzzy Logic and Particle Swarm Optimization Technique. Minerals Engineering 2010; 23(9): 713-719.
28. Miller JE, et al. Influence of Foot, Leg and Shoe Characteristics on Subjective Comfort. Foot & Ankle International 2000; 21(9): 759-767.
29. Mamdani EH, Assilian S. An Experiment in Linguistic Synthesis with A Fuzzy Logic Controller. International Journal of Man-Machine Studies 1975; 7(1): 1-13.
30. Zadeh LA. Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 1997; 90(2): 111-127.
31. Adnan MM, et al. Fuzzy Logic for Modeling Machining Process: A Review. Artificial Intelligence Review 2013; 1-35.
32. Haghighat E, Johari MS, Etrati SM, Tehran MA. Study of the Hairiness of Polyester-Viscose Blended Yarns. Part IV – Predicting Yarn Hairiness Using Fuzzy Logic. FIBRES & TEXTILES in Eastern Europe 2012; 20, 3(92): 39-42.
33. Chamundeswari G, Varma GP, Satyanarayana C. An Experimental Analysis of K-means Using Matlab. International Journal of Engineering 2012; 1(5).

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Bibliografia

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