Modeling of textile manufacturing processes using intelligent techniques: a review

Abstract

The development of manufacturing process modeling has attracted growing attention from the textile industry as the need for quickly exploring a textile manufacturing process is increasingly costly along with the growing complexity in the process. More and more researches shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of it is in line with the procedure of textile processes from yarn to fabrics, and to garments. The analysis and discussion of the previous studies are conducted from the different applications in a different process. The factors and performance properties considered in process modeling are collected in comparison. The considerations of the previous studies in terms of inputs' relative importance, feature selection, modeling techniques, data distribution and performance estimations are analyzed and summarized. On the basis of the summaries of more than 128 related articles from the point of views of textile engineering and artificial intelligence, the limitations, challenges and future perspectives in this issue are also concluded.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

“…The breadth of this field is extensive, encompassing investigations such as establishing standardized clothing sizes (Jeyasingh et al ., 2012), identifying factors that influence the insulation properties of garments (Jeyasingh et al ., 2012; Lee et al ., 2019), pinpointing variables that contribute to minimizing waste in glove production (Seçkin et al ., 2019), predicting the types of garments (lower body, upper body, whole body) and their subsets (blouse, dress, etc.) (Jain and Kumar, 2020; Cheng et al ., 2022), crafting a recommender system to assist customers in selecting clothing tailored to their physical attributes and purchasing history (Zhang et al ., 2021), ranking woven fabric defects observed during textile manufacturing (Saeidi et al ., 2013), monitoring yarn quality within a textile firm (Ertuğrul and Aytaç, 2009), modeling an intelligent manufacturing process line, encompassing textile processes from yarn to fabrics and garments (He et al ., 2021a), and exploring fashion trends within the realm of clothing (Shi et al ., 2021); all of which have been cited as noteworthy contributions. Table 2 summarizes research employing data mining methods in the yarns, textiles and clothing industries.…”

Touch sensors used in Industry 4.0 to machines in the manufacturing industry on occupational health and safety

“…Because cylinders can be used together with different machines. For example, calendaring is the process of passing the fabric between pressurized rollers to give shine to the surface between the yarns for softening and flattening textile products (He et al , 2021).…”

Recent Progress on Yarn‐Based Electronics: From Material and Device Design to Multifunctional Applications

“…[12] Yarn is an assembly of fibers that are arranged or twisted together to form a continuous strand. [13] Raw fiber material and twist technology are the two main factors affecting the mechanical strength of yarns. In the conventional spinning process, natural fibers such as cotton, wool, hemp, and silk are first formed into thick slivers via compacting, vortices, rings, or rotor spinning.…”