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Simulating the behavior of clothing has always been of interest in the apparel, fashion and computer game industries. With the development of these industries, there is a need to increase the accuracy of clothing simulation techniques. A garment contains many seams whose behavior affects its final appearance. In this study, a numerical model is presented to simulate seam puckers in single- and double-layer fabrics.

A yarn-level simulation technique has been used for this purpose. Based on this technique, the individual threads in the fabric structure and the sewing threads are modeled separately. Then, their behavior and interaction with each other are considered in the seam pucker model.

The model is used to simulate the real samples. The results show that the proposed model is able to simulate the degree of seam puckering for a single-layer fabric with an average error of 7.9% and for a double-layer fabric with an average error of 8.5%.

The behavior of the seam is affected by the properties, behavior and interaction of the sewing threads and yarns in the fabric structure. In previous studies, the parameters related to seams and fabrics were not fully considered. In this study, a new yarn-level model is presented to simulate seam puckering in woven fabrics. The most important advantage of this type of simulation is the ability to examine the interaction of fabric threads as well as the interaction of sewing threads with each other and with the threads of the fabric structure.

A series of all-atom molecular-level computational analyses is carried out in order to investigate mechanical transverse (and longitudinal) elastic stiffness and strength of p-phenylene terephthalamide (PPTA) fibrils/fibers and the effect various microstructural/topological defects have on this behavior. The paper aims to discuss these issues.

To construct various defects within the molecular-level model, the relevant open-literature experimental and computational results were utilized, while the concentration of defects was set to the values generally encountered under “prototypical” polymer synthesis and fiber fabrication conditions.

The results obtained revealed: a stochastic character of the PPTA fibril/fiber strength properties; a high level of sensitivity of the PPTA fibril/fiber mechanical properties to the presence, number density, clustering and potency of defects; and a reasonably good agreement between the predicted and the measured mechanical properties.

When quantifying the effect of crystallographic/morphological defects on the mechanical transverse behavior of PPTA fibrils, the stochastic nature of the size/potency of these defects was taken into account.

Fiber-reinforced armor-grade polymer-matrix composite materials with a superior penetration resistance are traditionally developed using legacy knowledge and trial-and-error empiricism. This approach is generally quite costly and time-consuming and, hence, new (faster and more economical) approaches are needed for the development of high-performance armor-grade composite materials. One of these new approaches is the so-called materials-by-design approach. Within this approach, extensive use is made of the computer-aided engineering (CAE) analyses and of the empirically/theoretically established functional relationships between an armor-grade composite-protected structure, the properties of the composite materials, material microstructure (as characterized at different length-scales) and the material/structure synthesis and fabrication processes. The paper aims to discuss these issues.

In the present work, a first step is made toward applying the materials-by-design approach to the development of the armor-grade composite materials and protective structures with superior ballistic-penetration resistance. Specifically, CAE analyses are utilized to establish functional relationships between the attributes/properties of the composite material and the penetration resistance of the associated protective structure, and to identify the combination of these properties which maximize the penetration resistance. In a follow-up paper, the materials-by-design approach will be extended to answer the questions such as what microstructural features the material must possess in order for the penetration resistance to be maximized and how such materials should be synthesized/processed.

The results obtained show that proper adjustment of the material properties results in significant improvements in the protective structure penetration resistance.

To the authors’ knowledge, the present work is the first reported attempt to apply the materials-by-design approach to armor-grade composite materials in order to help improve their ballistic-penetration resistance.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Discusses the 6th ITCRR, its breadth of textile and clothing research activity, plus the encouragement given to workers in this field and its related areas. States that, within the newer research areas under the microscope of the community involved, technical textiles focuses on new, ‘smart’ garments and the initiatives in this field in both the UK and the international community at large. Covers this subject at length.

The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn.

The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error.

The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy.

Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

The textile industry contributes 2 and 3% to the global and Indian Gross Domestic Product (GDP), respectively. India supplies a quarter of global cotton yarn. Yet, most yarn manufacturing companies use outdated methods and lack organisational skills and strategies. Improvement in processes in India could significantly help the industry worldwide.

The variables that influence the performance of the system were identified. Their interrelationships and impact were identified from the employees in the chosen case study, a yarn manufacturing industry. A System Dynamics (SD) approach was employed to study the benefits of implementing 5S lean strategies. The impact of each variable on various performance measures such as throughput, Work In Progress, processing time, waiting time, idle time, over-processing and scraps was analysed.

Improvement in outcomes reflected an enhanced adoption of leanness in the industry. The decision-makers can utilise this study to optimise the necessary parameters in the system and attain the desired productivity levels. Better resource management and reduced processing time helped increase the despatch rate by 9.735% and decrease the WIP by 23.01%. Time management helped to reduce the inventory, idle time and waiting time. Over-processing, defects and scraps were minimised, indicating a shift towards lean.

This study pioneers the use of SD simulation models for optimising yarn manufacturing using lean strategies. Improvement in performance measures by integrating these strategies opens avenues for future research using multiple approaches to address a problem.

Implementing 5S lean principles and simulations enhances productivity, reduces waste and optimises resource management for the yarn manufacturing industry. Decision-makers can employ simulation to witness the outcomes of their changes without investing cost and time and without associated implementation risks.

The use of a simulation model to witness the benefits of incorporating lean strategies in yarn production has not been explored. This approach could help the managers and policymakers understand their existing system's shortcomings and critical areas that require improvement.

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.

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.

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.

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.