Purpose

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

Design/methodology/approach

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

Findings

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

Originality/value

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

中文翻译：

---

为变形纱生产过程开发数据驱动的操作指南：数据挖掘和智能方法

目的

纺织工业在任何国家的经济中都具有巨大的重要性，特别是在合成纱线和织物的生产方面。因此，追求以较低的成本获得高质量的产品已成为各国的重大关切。本研究的主要目的是利用数据挖掘和数据智能技术，通过开发智能操作指南来增强和细化膨体纱的生产过程，该指南能够根据规格调整膨体纱制造过程中的生产工艺参数原材料。

设计/方法论/途径

本研究系统地回顾了文献，探讨了影响纱线质量的各种因素。采用数据挖掘技术，包括深度学习、K近邻（KNN）、决策树、朴素贝叶斯、支持向量机和投票，来识别最关键的因素。随后，利用 Power BI（商业智能）等数据智能工具开发了执行和动态指南。随后，所提出的模型被应用于伊朗一家纺织公司2020年至2021年的生产过程。

发现

本研究结果表明，生产工艺参数对变形纱质量的影响比原料特性更为显着。通过选择生产工艺参数（即拉伸比、D/Y 和初级温度）的最佳组合，并结合源自原材料特性的限制指标来预测强度和伸长率，设计了执行生产指南。

原创性/价值

本文介绍了一种创建动态指南的新方法。提出了一种膨体纱生产中强力和伸长率的智能动态指导方法，准确率约为80%。该开发指南是动态的，并且与生产数据库无缝集成。它每三个月定期更新一次，纳入所选的工艺和原材料特征、各自的阈值以及预测的伸长率和韧性水平。