Effect Of Fiber Geometrical Characteristics On Yarn Unevenness

Study on the effect of fiber properties on yarn performance has been one of the classical issues in the textiles'fundamental theories. Researchers have been concentrating on measuring and evaluating the fiber properties, configuring and optimizing the processing parameters, and analyzing and predicting the yarn properties. Among the measures of yarn performance, the yarn unevenness is the significant indicator which is closely correlated with and affecting the other parameters. By analyzing the random alignment of fibers with certain geometrical characteristics (length and fineness) in fiber assembly, we could promote the understanding of the mechanism of yarn unevenness (including theoretical unevenness and additional unevenness), which provides valuable and theoretical reference to optimize the assorting of raw materials and processing parameters, and to predict the yarn properties analytically.Starting by evaluating the probability density function of fiber geometrical characteristics, this thesis realizes to characterize the entire distributions and to carry out the calculation precisely. During the model development for analyzing the effect of fiber random alignment on yarn unevenness, the fibers are assumed to be straight and parallel along the sliver. The work of this thesis covers the following parts:a) characterization of the probability density function of fiber length and fineness distributions; b) effect of fiber length and fineness distributions on yarn unevenness; c) effect of fiber length distribution on drafted sliver irregularity; d) effect of fiber length and fineness distributions on yarn unevenness and blend irregularity in blended yarns, which correspond to Chapters Two to Fiver in the dissertation.In order to investigate fiber geometrical random alignment in sliver, the fiber geometrical characteristics need exploring. Based on fiber length histogram, Chapter Two firstly presents an approach to fit the density function of cotton fiber length distribution using a non-parametric kernel estimate. The goodness of fit is verified by comparing the calculated and tested length parameters, and the analysis results in the conclusion that the normal kernel function is superior and generating the more smoothing curve of the length density function. Since the length parameters are still the dominant measures in practical, the correlation between fiber length statistics (mean & variance) and length measures is identified by employing the stepwise regression analysis, and then a finite mixture model, combining normal distribution and power function, is adopted to generate the probability density function of fiber length. It is shown that the calculated short fiber contents and effective length from the probability density function fit the experimental values well. Based on the differences of fiber length histogram by weight and by number determined by aQura Cotton Fiber Length Testing Instrument, the fiber fineness distribution under different lengths is deduced. The coefficient of variation of fiber fineness and diameter are characterized comprehensively with the known variance of linear density of upland cotton fiber along its longitudinal direction from the length distributions. Also, the illustrative application of fiber length density function in analyzing fiber length changes in stretch-breaking technique verifies the feasible approach in utilizing theoretical analysis, which provides a plausible way in spinning control and predicting & deducting the yarn properties.In Chapter Three, after briefly introducing the classical theories from Martindale and Suh on yarn unevenness which were constructed on fiber fineness and length distribution respectively, the limitations are reported as well. Given cotton samples, the kernel function is used to estimate the probability density functions of fiber length, therefore, the calculation of Suh's model for theoretical unevenness is facilitated. The statistical influence of fiber length parameters on yarn theoretical and additional unevenness is discussed. It can be concluded that decreasing the values of effective length, length irregularity and 16mm SFC by weight would improve the yarn uniformity, especially for the carded spun yarns. Since fiber length distribution and fineness exert considerable influence on fiber random alignment and yarn unevenness. This thesis also presents the number of fibers per yarn cross-section in terms of fiber length distribution and fiber end density, and develops a new model for the yarn theoretical unevenness. Compared with the results from Martindale's and Suh's theory, the change tendency of the calculated values from the new expression is much closer to the tested.The additional unevenness of yarn is mainly generated in drafting, on account of the variable drafting force and the irregular fiber movement which adversely affect the geometrical arrangements of fibers in the sliver. In Chapter Four, utilizing the data collected from I.T.T. (Institute of Textile Technology, USA) Draftometer, the drafting processes in both break and main draft zones are simulated, and the effects of draft ratio and ratch on drafting force and the coefficient of variation (CV) of drafting force are analyzed. It is shown that in break draft zone, there exists a characteristic point in drafting force which leads to the least CV of drafting force; as the ratch increases to a certain level, the CV of drafting force increases slightly. In the main draft zone, the tested mean drafting force reflects the effect of break draft, which confirms the decisive role of break draft. The ratch affects the CV of drafting force and wider ratches yield more variation. The low CV of drafting force promises better sliver regularity. The irregular fiber movement directly causes the sliver unevenness, during the second part in this Chapter, a 2-parameter Log-normal distribution is introduced to estimate the probability density function of the fiber accelerated point by considering the influence of draft ratio and ratch, as well as fiber lengths, and the model is experimentally proved. With regards to the relationship between accelerated point distribution and sliver irregularity, a numerical simulation method is proposed to optimize the draft settings using the minimum coefficient of variation (CV) of fiber accelerated point. The new approach offers advantages over the traditional regressed optimal drafted settings (to yield the least weight variation in drafted slivers) and does not need extensive laboratory trials.As for the blended yarns, the fiber fineness ratio of two components fibers is introduced in Chapter Five, and the relation between yarn theoretical unevenness based on Martindale theory and fiber fineness and its irregularity is graphically analyzed. The result is that when the fineness ratio is between 0.2 and 0.3, the lowest theoretical unevenness of yarn could be obtained. Moreover, a new model, adopting conditional probability, is developed to evaluate the theoretical blend irregularity of a two-component blended yarn. By introducing the variation of the number of fibers per cross-section along the yarn, the obtained expression can offer insight into the cause of blend irregularity. Two experiments show that the results from the new model have essentially the same changing tendency as experimental values.To sum up, this dissertation constructs the probability density function of fiber length and fineness distributions which covers the information of the entire distributions, studies the effect of fiber geometrical characteristics on fiber random alignment in fiber assembly, and exposits the principles of fiber movement in drafting and generation of yarn unevenness, in order to offer the theoretical and quantitative methods to optimize the assorting of raw materials and technical settings, and to analytically predict the yarn properties.