The paper proposes a method for characterizing the in-situ interaction between filaments in a multifilament yarn. The stress transfer between neighboring filaments causes the reactivation of a broken filament at some distance from the break. The utilized statistical bundle models predict a change in the slope of the mean size effect curve once the specimen length becomes longer than the stress transfer length. This fact can be exploited in order to determine the stress transfer length indirectly using the yarn tensile test with appropriately chosen test lengths. The identification procedure is demonstrated using two test series of tensile tests with AR-glass and carbon yarns.

The paper proposes a method for characterizing the in-situ interaction between filaments in a multifilament yarn. The stress transfer between neighboring filaments causes the reactivation of a broken filament at some distance from the break. The utilized statistical bundle models predict a change in the slope of the mean size effect curve once the specimen length becomes longer than the stress transfer length. This fact can be exploited in order to determine the stress transfer length indirectly using the yarn tensile test with appropriately chosen test lengths. The identification procedure is demonstrated using two test series of tensile tests with AR-glass and carbon yarns.

The degradation of wool keratin under conditions relevant to those of wool dyeing was investigated using the techniques of gel permeation chromatography (GPC), ion exchange gel chromatography, and amino acid analysis. Physical testing of the treated and untreated wool was also carried out to determine the physical changes occurring, parameters used being percentage elongation at the break, and the breaking strain of the fibre. Samples of wool keratin were immersed in various aqueous solutions at 1000C for 24 hours and the filtered, aqueous, oxidised extracts were analysed* The solutions used varied only in the dye, or dye intermediate present in the treatment solution. All treatment baths contained 10% owf 1.02 x 10 -2 MSulphuric VI acid; 10%owf 7.04x 10 -3 MSodium sulphate VI ; A 100 :1 liquor ratio was used in each case. Some of the dye intermediates showed a marked catalytic effect, particularly in their effect on breaking strain, a decrease of 40% in some cases. The GPC profiles of the extracted proteins were examined in detail and compared against previous workers' results. An explanation of the behaviour of the dyes and intermediates was proposed. The amino acid composition data of the extracted and fractionated proteins were compared against various morphological components extracted by other workers, as was the total gelatin obtained from each treatment.

The degradation of wool keratin under conditions relevant to those of wool dyeing was investigated using the techniques of gel permeation chromatography (GPC), ion exchange gel chromatography, and amino acid analysis. Physical testing of the treated and untreated wool was also carried out to determine the physical changes occurring, parameters used being percentage elongation at the break, and the breaking strain of the fibre. Samples of wool keratin were immersed in various aqueous solutions at 1000C for 24 hours and the filtered, aqueous, oxidised extracts were analysed* The solutions used varied only in the dye, or dye intermediate present in the treatment solution. All treatment baths contained 10% owf 1.02 x 10 -2 MSulphuric VI acid; 10%owf 7.04x 10 -3 MSodium sulphate VI ; A 100 :1 liquor ratio was used in each case. Some of the dye intermediates showed a marked catalytic effect, particularly in their effect on breaking strain, a decrease of 40% in some cases. The GPC profiles of the extracted proteins were examined in detail and compared against previous workers' results. An explanation of the behaviour of the dyes and intermediates was proposed. The amino acid composition data of the extracted and fractionated proteins were compared against various morphological components extracted by other workers, as was the total gelatin obtained from each treatment. / Science Research Council

This master thesis focuses on applying deep neural networks (DNNs) in image-based condition monitoring of air-jet spinning machines, specifically focusing on the spinning pressure parameter. The study aims to develop a sensor system to detect structural defects in yarns and assign them to specific machine conditions. The research explores using DNNs to analyze images of yarns generated at different spinning pressures within the spinning box to create a rich dataset for training deep learning models. The study also evaluates the effectiveness of the DNN-based approach in detecting and classifying structural defects in yarns and determining the corresponding machine conditions. The outcomes of this research could potentially help textile enterprises improve the quality and efficiency of their yarn manufacturing processes.