@article{yitik_optimisation_2025,
	title = {Optimisation of combed yarn properties based on yarn number and machine jaw range using artificial neural networks},
	volume = {76},
	issn = {1222-5347},
	url = {https://revistaindustriatextila.ro/images/2025/6/013%20BEKIR%20YITIK_%20INDUSTRIA%20TEXTILA%20no.6_2025.pdf},
	doi = {10.35530/IT.076.06.20257},
	abstract = {The need for natural clothing is increasing day by day. To meet this demand, the apparel industry is developing new
systems to enhance production and raw material usage. Using healthy products is essential for a healthy life, which
increases the need for natural raw materials. Cotton is the ideal natural raw material for a renewable and sustainable
production line. Despite the growing production, it cannot fully meet the demand. Therefore, new systems are being
developed to improve the quality of cotton production. The foundation of the textile industry is yarn, and yarn production
lines consist of systematically operated machines. These production systems include carded, combed, and open-end
methods. In combed production, high-quality and long fibres are used to produce yarns with counts such as Ne 30 or
Ne 50. In combed yarn production, fibre length and ratio can be adjusted through machine settings. Lap feeding cylinder
gaps in combed yarn machines are critical for this adjustment. In this study, experimental results were obtained using
4 different yarn counts produced from the same blend and 5 different combed feeding jaw settings. These results were
optimised using artificial neural networks. In the analysis, yarn count and combing cylinder gap were used as input data,
while the physical properties of the yarn were used as output data.},
	number = {06},
	urldate = {2026-01-01},
	journal = {Industria Textila},
	author = {Yitik, Bekir},
	month = dec,
	year = {2025},
	pages = {868},
}