Thesis (M.S. in T. & F.E.)--School of Textile and Fiber Engineering, Georgia Institute of Technology, 2004. Directed by Youjiang Wang. / Includes bibliographical references (leaves 92-94).

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The development of cottons with improved fiber quality has been a major objective in breeding programs around the world. Breeders have focused their attention on improving fiber strength and length, and have generally not used fiber elongation in the selection process. Although literature has reported a negative correlation between fiber elongation and tenacity, this correlation is weak and should not prevent breeders from simultaneously improving fiber tenacity and fiber elongation. Furthermore, the work of rupture property, important in the spinning process, could be best enhanced by improving both fiber tenacity and fiber elongation. Fifteen populations were developed in 2007 by crossing good quality breeding lines with high elongation measurements to ‘FM 958’; a High Plains standard cultivar with good fiber quality but reduced elongation. Samples in every generation were ginned on a laboratory saw gin, and the lint was tested on HVI (High Volume Instrument). The F2 and F3 generations showed a wide range of variation for elongation (6.9% - 12.8% for the F2 and 4% - 9.20% for the F3) allowing divergent selection for low and high fiber elongation. A correlation (r) of -0.32 between strength and elongation was observed in the F2 individual plant selections. In the F3, the correlation (r) between strength and elongation was -0.36, and in the F4 the correlation (r) was -0.08. Nine lines were selected from the original 15 populations for spinning tests. The correlation between fiber elongation and strength for these lines was positive (r=0.424), indicating that with targeted selection, fiber elongation and strength can be simultaneously improved. Fiber elongation was positively correlated with yarn tensile properties tenacity (r=0.11), work-to-break (r=0.68) and breaking elongation (r=0.87); and was negatively correlated with yarn evenness properties, number of thin places (r=-0.16), number of thick places (r=-0.9), nep count (r=-0.24), hairiness (r=-0.38) and total number of imperfections (r=-0.38). All selections for high elongation were superior for all tensile properties compared to the low selections and the check in the analysis over locations and in each location. Furthermore, selections for high elongation were significantly different from the selections for low elongation and the check. In addition to developing lines for fiber spinning tests with improved, or differentiated, fiber elongation, this project was amended to evaluate and determine the heritability of fiber elongation. Three different methodologies were used to obtain estimates of heritability; variance components, parent off-spring regression, and realized heritability using F3, F4, and F5 generation. No inbreeding was assumed because there was no family structure in the generations within this study. Estimates of heritability by the variance component methods in the F3, F4 and F5 were 69.5%, 56.75% and 47.9% respectively; indicating that 40-50% of the variation was due to non-genetic effects. Parent off-spring regression estimates of heritability were 66.1% for the F3-4 and 62.8% for the F4-5; indicating a high resemblance from parents to off-spring. Estimates of realized heritability were obtained to determine the progress realized from selection for the low and high selection for fiber elongation. Estimates were intermediate (0.44–0.55), indicating moderately good progress from selection. The results from this project demonstrate that it is possible to improve fiber elongation and to break the negative correlation between elongation and strength. Furthermore, it has been demonstrated that improving fiber elongation results in the increase of length uniformity index and decreased short fiber content. Additionally, directed divergent selection was a successful methodology for the improvement of fiber elongation, and was useful to demonstrate that higher fiber elongation has a positive effect on yarn tensile properties, yarn evenness and processing. The development of new cultivars with improved fiber elongation will improve the quality and reputation of U. S.-grown cotton. The ultimate result will be better yarn quality and improved weaving efficiency, and particularly address current weaknesses in U. S. –grown cotton cultivars, especially from the High Plains of Texas, of more short fiber content, lower uniformity ratios, and weaker yarn strength.

The optimization of the spinning process and adjustment of the machine settings involve “Trial and Error” method resulting in the wasting of production time and material. This situation becomes worse in the spinning mills where the speed and material changes are frequent. This research includes the use of artificial neural networks to provide the thinking ability to the spinning machines to improve the yarn spinning process. Draw frame, being the central part of the spinning preparation chain and last machine to rectify the variations in the fed slivers is the main focus of the research work. Artificial neural network have been applied to the leveling action point at auto-leveler draw frame and search range of leveling action point has been considerably reduced. Moreover, the sliver and yarn characteristics have been predicted on the basis of draw frame settings using the artificial neural networks. The results of present research work can help the spinning industry in the direction of limiting of “Trial and Error” method, reduction of waste and cutting down the time losses associated with the optimizing of machines. As a vision for the future research work the concept of intelligent spinning machines has also been proposed. / Die Optimierung des Spinnprozesses und die Maschineneinstellung erfolgen häufig mittels „Trial und Error“-Methoden, die mit einem hohen Aufwand an Produktionszeit und Material einhergehen. Diese Situation ist für Spinnereien, in denen häufige Wechsel des eingesetzten Materials oder der Produktionsgeschwindigkeit nötig sind, besonders ungünstig. Die vorliegende Arbeit zeigt das Potenzial Neuronaler Netze, um die Spinnmaschine zum „Denken“ zu befähigen und damit die Garnherstellung effektiver zu machen. Die Strecke ist der zentrale Teil der Spinnereivorbereitungskette und bietet die letzte Möglichkeit, Inhomogenitäten im Faserband zu beseitigen. Der Fokus der Arbeit richtet sich deshalb auf diese Maschine. Künstlich Neuronale Netze werden an der Strecke zur Bestimmung des Regeleinsatzpunktes genutzt, womit eine beträchtliche Reduzierung des Aufwands für die korrekte Festlegung des Regeleinsatzpunkts erreicht wird. Darüber hinaus können mit Hilfe der Neuronalen Netze die Band- und Garneigenschaften auf Basis der Streckeneinstellungen vorausbestimmt werden. Die Resultate der vorliegenden Arbeit machen „Trial und Error“-Methoden überflüssig, reduzieren den Ausschuss und verringern die Zeitverluste bei der Maschinenoptimierung. Als Zukunftsvision wird eine Konzeption für intelligente Spinnmaschinen vorgestellt.

The optimization of the spinning process and adjustment of the machine settings involve “Trial and Error” method resulting in the wasting of production time and material. This situation becomes worse in the spinning mills where the speed and material changes are frequent. This research includes the use of artificial neural networks to provide the thinking ability to the spinning machines to improve the yarn spinning process. Draw frame, being the central part of the spinning preparation chain and last machine to rectify the variations in the fed slivers is the main focus of the research work. Artificial neural network have been applied to the leveling action point at auto-leveler draw frame and search range of leveling action point has been considerably reduced. Moreover, the sliver and yarn characteristics have been predicted on the basis of draw frame settings using the artificial neural networks. The results of present research work can help the spinning industry in the direction of limiting of “Trial and Error” method, reduction of waste and cutting down the time losses associated with the optimizing of machines. As a vision for the future research work the concept of intelligent spinning machines has also been proposed. / Die Optimierung des Spinnprozesses und die Maschineneinstellung erfolgen häufig mittels „Trial und Error“-Methoden, die mit einem hohen Aufwand an Produktionszeit und Material einhergehen. Diese Situation ist für Spinnereien, in denen häufige Wechsel des eingesetzten Materials oder der Produktionsgeschwindigkeit nötig sind, besonders ungünstig. Die vorliegende Arbeit zeigt das Potenzial Neuronaler Netze, um die Spinnmaschine zum „Denken“ zu befähigen und damit die Garnherstellung effektiver zu machen. Die Strecke ist der zentrale Teil der Spinnereivorbereitungskette und bietet die letzte Möglichkeit, Inhomogenitäten im Faserband zu beseitigen. Der Fokus der Arbeit richtet sich deshalb auf diese Maschine. Künstlich Neuronale Netze werden an der Strecke zur Bestimmung des Regeleinsatzpunktes genutzt, womit eine beträchtliche Reduzierung des Aufwands für die korrekte Festlegung des Regeleinsatzpunkts erreicht wird. Darüber hinaus können mit Hilfe der Neuronalen Netze die Band- und Garneigenschaften auf Basis der Streckeneinstellungen vorausbestimmt werden. Die Resultate der vorliegenden Arbeit machen „Trial und Error“-Methoden überflüssig, reduzieren den Ausschuss und verringern die Zeitverluste bei der Maschinenoptimierung. Als Zukunftsvision wird eine Konzeption für intelligente Spinnmaschinen vorgestellt.

Varje år kasserar Försvarets materielverk (FMV) en stor mängd av soldatutrustning av textil. Den textila soldatutrustningen kasseras med anledning att den inte längre går att laga eller har förlorat sin funktion och skickas därmed till förbränning. I enlighet med FMV:s miljöpolicy 2020 vägs ekonomiska och tekniska krav mot mer hållbara alternativ för att bli en mer miljömässigt hållbar verksamhet. Med avsikt att ytterligare utveckla sitt hållbarhetsarbete vill FMV utreda möjligheterna till textilåtervinning för att förlänga livslängd av textila material ytterligare. Därav har denna rapport behandlat mekanisk återvinning av textila soldatutrustningar samt garnspinning av återvunna fibrer. Arbetets bedrivs med syfte att redogöra en studie på uppdrag av FMV med fokus på möjligheter till hur mekanisk återvinning genomförs av textila soldatutrustning och hur garn kan spinnas av fibrer från återvunna fibrer. Genom en litteraturstudie har faktorer som påverkar mekanisk återvinning av textila material samt lämpliga garnspinningsmetoder för återvunna fibrer undersökts i detta arbete. En analys har även genomförts med fokus på fyra klädesplagg fån FMV: skjorta, t-tröja, långkalsong, och stickad tröja. De parametrar som i högsta grad behöver tas hänsyn till för att uppnå så hög garnkvalité är fiberblandning, samt inställningar för spinning, sträckning och kardning. Vid garnspinning är fiberlängden av stor betydelse och därav behöver den mekaniska återvinningen utföras på ett så skonsamt sätt som möjligt för att minska fiberslitaget. För att spinna garn av återvunna fibrer är rotorspinning och friktionsspinning de mest lämpliga metoder, men garnet som produceras har en hög grovlek. För en högre garnkvalité behövs en blandning av jungfruliga fibrer med återvunna fibrer för ett mer tillfredsställande resultat. / Each year, Försvarets Materielverk (FMV) discards a massive amount of textile soldier equipment no longer possible to repair and unqualified to use and are therefore incinerated. In accordance with FMV:s policy for environmental sustainability 2020, economic and technical requirements have to be considered in relation to more sustainable alternatives in order to become a more environmentally sustainable organization. With the purpose to further develop towards more sustainable practices, FMV are interested in investigating the possibilities for mechanical textile recycling and yarn spinning of the re-cycled fibers to extend the life cycle of textile materials. The report’s goal was to focus on possibilities and challenges considering the following garments assigned by FMV: t-shirt, knitted sweater, shirt and thermal underwear. The method is based on a literature study and examination of the garments.Conclusions drawn from the study are that fiber blend and settings for carding, drawing and spinning are crucial to produce yarn of high quality. Especially the fiber length has an impact on the possibilities of yarn spinning since it needs to be long enough. To spin yarns from recycled fibers, open-end spinning methods such as rotor spinning and friction spinning are the most suitable. Furthermore, recycled fibers need to be blended with virgin fibers to enable spinning.