This study investigates the working principle of compact spinning technology and the structural differences between Com4® yams spun by a Rieter ComforSpin® K40 compact spinning machine and conventional ring spun yarns. Differences in yam quality are observed. The higher tensile strengths and lower hairiness of the Com4 yarns are the key achievements of the compact spinning technology. However, limitations of the new technology are also revealed by investigating the quality of yams with different counts.

This paper presents a theoretical analysis of fiber tension distribution within a ring spun yarn without relaxation. Yarn residual torque is determined on the basis of the translation of fiber tension at the spinning triangle, as presented in the first part of this series of papers, into the fiber tension within the yarn. The results from numerical simulations indicate that, with fiber buckling, the yarn exhibits a lower average fiber tension and, thus, a much reduced yarn residual torque than that without fiber buckling. Comparison with experiments confirmed that fiber buckling exists in ring yarns while the assumption of no fiber buckling is not realistic. Generally, a low yarn twist results in low average fiber tension in the yarn and, thus, a reduced yarn residual torque. A symmetrical spinning triangle leads to a slightly higher yarn residual torque than a right-angle spinning triangle when the yarn counts and twists are identical and fiber buckling occurs.

In the production of cotton and polyester JetRing spun yarns, spindle speed, air pressure, and yarn twist level affect yam hairiness. However, the distance between the front roller nip and the nozzle inlet has only a marginal effect on yam hairiness. Results also show that JetRing spun yams have a much lower numbers of hairs than the equivalent ring spun yams.

For combed cotton yarns, the wider the strand spacing is in Sirospun, the better the yarn properties are in terms of tenacity, Uster hairiness, abrasion resistance, and trapped strand twist.

The present paper proposes an artificial neural network model for the prediction of the degree of spirality of single jersey fabrics made from 100 % cotton conventional and modified ring spun yarns. The factors investigated were the yarn residual torque as the measured twist liveliness, yarn type, yarn linear density, fabric tightness factor, the number of feeders, rotational direction and gauge of the knitting machine and dyeing method. The artificial neural network model was compared with a multiple regression model, demonstrating that the neural network model produced superior results to predict the degree of fabric spirality after three washing and drying cycles. The relative importance of the investigated factors influencing the spirality of the fabric was also investigated.