The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research

Weaving saturation can have harmful consequences, such as problems with loom performance, accelerated wear of mechanical parts and loss of raw materials. To avoid these problems, when designing and creating new fabrics, the densities and yarn qualities must be carefully matched with the weaves to ensure successful testing. To facilitate this task, this study focuses on the development of a practical fuzzy logic model for predicting the saturation of new fabrics. An experimental part was carried out to validate this fuzzy model. The fabric samples used in this study came from three different types of weaves, namely plain, twill and satin. These samples also included five weft counts (Nm) and eight different densities. The results obtained using the fuzzy logic model developed were compared with experimental values. The prediction results were satisfactory and precise, demonstrating the effectiveness of the fuzzy logic model developed. The mean absolute error of the calculated fuzzy model was 1,97 %. It was therefore confirmed that this fuzzy model was both fast and reliable for predicting the saturation of new fabric

A company's performance depends on the efficiency of its supply chain. In an environment where supply chains become more complex, it is important to evaluate supplier performance.This process is challenging due to the multiplicity of criteria to be considered and the involvement of several experts with different interpretations and judgments, which classifies it as a multi-criteria decision-making (MCDM) problem.In order to address this complexity, the objective of this article is to develop a model based on fuzzy logic that will facilitate this evaluation by supporting managers in their decision-making processes related to the qualification or disqualification of suppliers.To illustrate the application of fuzzy logic, a case study was conducted within a company operating in the mining sector with the aim of evaluating several suppliers on the basis of five evaluation criteria: delivery time, quality, staff behavior, and commitments to quality, hygiene, safety, the environment, and corporate social responsibility. The fuzzy logic model was used to process the evaluations from experts and to calculate the performance level of each supplier.In order to validate this model, the results of the fuzzy evaluation were compared with those from the company’s original method. The comparison showed that the fuzzy model gives consistent and relevant results that reflect the company’s real practices.The study shows that fuzzy logic can improve supplier evaluation by handling complex situations and supporting fair and balanced managerial decisions.

One of the most important characteristics of a warp and weft fabric is its tensile strength. The aim of this research is to develop a practical fuzzy logic model that could anticipate the ideal tensile strength of new fabrics by modifying only the weave structure. An experimental part was carried out on different weave structures to obtain the results that will enable the development of this new model. We then used the fuzzy logic model to compare its results with those of the experimental tests. The calculated mean absolute error of the fuzzy model was 1.83% for tensile strength in the warp direction and 1.99% for tensile strength in the weft direction. This result also confirmed that the fuzzy model was not only effective, but also reliable in predicting the strength of the new fabric.

The transition towards environmentally friendly and sustainable materials has intensified interest in natural fiber-reinforced composites, with sisal fibers standing out due to their biodegradability and mechanical performance. Despite these advantages, their practical use remained hindered by poor interfacial adhesion and high moisture uptake, largely attributed to their hydrophilic nature and surface impurities.In this study, a dual chemical treatment using sodium hydroxide (NaOH) followed by potassium permanganate (KMnO₄) was applied to three types of woven sisal fabrics (plain, twill, and satin) to enhance fiber–matrix interaction and overall composite properties. Twenty-seven composite variants were produced and evaluated to investigate the influence of weave structure, treatment concentration, and immersion time on tensile strength and water absorption.To capture the intricate relationships between these variables, a fuzzy logic-based predictive model was developed. This model effectively forecasted material behavior, achieving low average absolute errors of 1.77% for tensile strength and 3.46% for water absorption, demonstrating its robustness and value as a tool for process optimization.This study contributed not only to the development of high-performance, bio-based textile composites, but also introduced an intelligent, cost-effective predictive framework capable of reducing experimental demands while guiding sustainable material development.

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies.