Thesis Details

Thesis Title: The Effects of Boll Position on Micronaire Prediction and Fiber Characteristics
Thesis Author: Gary Bishop
Abstract: Textile operations constantly deal with variation in raw materials. This work has concentrated on understanding where variation arises and how it may be diminished. Since micronaire is so important to processing in both yarn production and dyeing, the average micronaire of four samplings was used to help predict what the average micronaire of the field was at the point of sampling. The data was used to predict when to apply defoliant to the cotton crop. Studies show that micronaire values stop when defoliants are applied. By using micronaire as a monitoring tool, growers can know when to apply defoliant to give them the micronaire desired by the textile manufacturers. Preliminary results indicate the use of micronaire as a tool for knowing when to apply defoliant will indeed aid the cotton producer in managing his crop. The data point to this method as being accurate to plus or minus two-tenths of a micronaire unit for the overall average of the field. The other objective of this study was to find where the problem cotton originates on the plant. Cotton was hand picked by position and kept separate throughout all processing. HVI testing indicates significant differences in the fiber properties of micronaire, uniformity index, Rd, and +b. Interestingly, the fiber maturity data using near-infrared analysis followed the same line as the micronaire numbers. Open-end yarn produced from the fiber also showed significant differences in percent coefficient of variation by position. After the yarns were knitted into socks, dyeing of the material showed significant differences in dye uptake according to position. The above results provide information for the cotton producer when deciding how to manage his crop. Crop management programs can be designed to maximize boll development at positions which supply the best cotton fiber to the textile industry. The results also indicate the importance of using data compiled from a large sampling such as module averaging.