Supply chain traceability has emerged as a prime requirement for multi-tier supply chains. It not only enables the supply chain visibility but also caters to the consumer requirements related to transparency, quality assurance, and production tracking. Nonwoven supply chain is one such example that particularly requires traceability implementation due to prevailing problems related to information asymmetry and complex supply chain networks. Conversely, it is challenging for supply chain partners to share all the competitive information in the unsecure environment. In this context, in line with Industry 4.0, this study investigates blockchain technology, which uses a shared and secured data infrastructure to keep track of information about assets and requires no central authority to function. It further proposes a blockchain-based traceability framework that explains supply chain partner interaction and network architecture at organizational level and smart contract and transaction validation rules at the operational level. In order to illustrate the application of the framework, the study presents an example of a nonwoven supply chain to track the nonwoven manufacturing and distribution processes. The proposed system can build a technology-based trust among the supply chain actors, where the distributed ledger would be used to store and authenticate of supply chain transactions.

Traceability has emerged as a prime requirement for a multi-tier and multi-site production. It enables visibility and caters to the consumer requirements of transparency and quality assurance. Textile and clothing industry is one such example that requires traceability implementation to address prevailing problems of information asymmetry and low visibility. Customers find it difficult to access product data that can facilitate ethical buying practices or assure product authenticity. Besides, it is challenging for stakeholders to share crucial information in an insecure environment with risk of data manipulations and fear of losing information advantage. In this context, this study investigates and proposes a blockchain-based traceability framework for traceability in multi-tier textile and clothing supply chain. It conceptualizes the interaction of supply chain partners, and related network architecture at the organizational level and smart contract and transaction validation rules at the operational level. To illustrate the application of the proposed framework, the study presents an example of organic cotton supply chain using blockchain with customized smart contract and transaction rules. It finally demonstrates the applicability of the developed blockchain by testing it under two parameters. The proposed system can build a technology-based trust among the supply chain partners, where the distributed ledger can be used to store and authenticate supply chain transactions. Further, the blockchain-based traceability system would provide a unique opportunity, flexibility, and authority to all partners to trace-back their supply network and create transparent and sustainable supply chain.

Blockchain has emerged as a prominent and reliable solution that can enable and ensure secure information sharing over wide area networks. In an era of digitalisation, blockchain technology is finding wide applications in multiple fields including implementing traceability in the supply chain. In this direction, this chapter explores its potential application in implementing a blockchain-based traceability system for textile and clothing (T&C) supply chain. It examines the necessity and concept of a traceability system, followed by enlisting advantages of blockchain technology for implementing traceability. Further, a case-based example has been used to explain blockchain application in implementing traceability in T&C supply chain. Finally, it mentions the challenges and limitations of such blockchain-based traceability system that can be addressed through further research.

The purpose of this paper is to understand supply network configuration for small-series production within high-cost contexts, and the context-specific decision logics associated. A total interpretive structural modelling (TISM) and MICMAC mixed-methods approach is used to determine and interpret interrelationships among SNC and capability-related aspects identified from the literature. Respondents come from EU textile/apparel companies, undertaking small-series production/sourcing in the region, with different roles in the value chain. The findings led to several propositions. They highlight the foundational nature of supply chain relationships and digital data sharing; interacting product/process flexibility and specialisation considerations, with associated enablers and barriers; the challenges related to location, which is the crucial supply chain driver; and the need to balance various interrelated capability drivers, such as quality, innovation, and sustainability. These findings can support practitioners for reconfiguration, and the approach can be used to address other contexts and thus enhance generalisability.

The increasing emphasis on small series production in higher cost locations is particularly challenging in the textile/apparel industry due to global supply chain configurations. To support (re)configuration in this context, Total interpretive structural modelling (TISM) method is used to gain a deeper understanding of the inter-relationships among configuration/capability related aspects from the literature. TISM interviews were done with 24 industry practitioners producing/sourcing in the EU, or in the process of implementation. The findings validate extensive inter-relations, at the same time highlighting relational aspects, internal integration, and quality as significant drivers. Further in-depth research is required to understand these influences.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

This paper studies the textile manufacturer–buyer relationship under quality inspection and traceability policy using non-cooperative Stackelberg game models. These models consider constant demand and price function, whereas the manufacturer and buyer regulate their profits by controlling product quality, transfer payment, quality inspection and traceability.

Exploring fully integrated textile tags and information systems for implementing traceability in textile supply chains

Traceability, in general, refers to keeping track of information to a certain degree. The concept of traceability is considered important to verify the various aspects of products in different industries and their global supply chains. Textile industry is among those which are accused time‐to‐time for opaque supply chains and unsustainable practices. Particularly, the aftermath of a series of industrial catastrophes, customers and non‐government organizations have started to scrutinize the brands to bring transparency in their supply chains. In this direction, traceability has been identified as a tool for organizations to trace their supplies throughout the supply chains and collect relevant information to ensure transparency and claim validation. Traceability has been further acknowledged as a competitive element and often acts as a decisive factor in purchase process. Moreover, the textile products are one of the most counterfeit‐prone items around the world. As a result, the demand for traceability has been intensified for supply chain monitoring and security, and product authentication.

The principal aim of this thesis was to address the implementation of traceability information systems in the textile supply chain. Further, it investigates the feasibility of yarn‐based integrated tracking tags as a means to impart traceability in textiles. It has been pursued through several independent studies in the domain of textile manufacturing, supply chain management and information systems. The appended papers in this thesis address various aspects of traceability implementation in the textile supply chain and how traceability information can be encoded into the textiles using yarn‐based coding.

Traceability consists of two components namely information system and tagging. The latter component is used to uniquely identify the product in the supply chain which assists in recalling and/or storing the relevant traceability data from/in the information system. Different actors in the supply chain manage the traceability data in their information systems, therefore traceability tag acts as a linking agent for information exchange. In this direction, this thesis introduces the concept of yarn coding and yarn coding‐based integrated tags which can be potentially used in future for textile traceability applications. In addition, a framework is proposed for the implementation of traceability information system in the textile supply chain. The work highlights various elements which can play a significant role in promoting and/or implementing traceability. Regardless of perspective or viewpoint, traceability is interwoven between technical and managerial aspects; therefore traceability implementation requires a techno‐managementapproach to obtain an optimal solution.

Textile and clothing (T&C) products contribute to a substantial proportion of the non-food product recalls in the European Union (EU) due to various levels of associated risks. Out of the listed 34 categories for product recalls in the EU’s Rapid Exchange of Information System (RAPEX), the category ’clothing, textiles, and fashion items’ was among the top 3 categories with the most recall cases during 2013–2019. Previous studies have attempted to highlight the issue of product recalls and their impacts from the perspective of a single company or selected companies, whereas limited attention is paid to understand the problem from a sector-specific perspective. However, considering the nature of product risks and the consistency in a higher number of recall cases, it is important to analyze the issue of product recalls in the T&C sector from a sector-specific perspective. In this context, the paper focuses on investigating the past recalls in the T&C sector reported RAPEX during 2005–2021 to understand the major trends in recall occurrence and associated hazards. Correspondence Analysis (CA) and Latent Dirichlet Allocation (LDA) were applied to analyze the qualitative and quantitative recall data. The results reveal that there is a geographical pattern for the product risk that leads to the recalls. The countries in eastern part of Europe tend to have proportionately high recalls in strangulation and choking-related issues, whereas chemical-related recalls are proportionately high in countries located in western part of Europe. Further, text-mining results indicate that design-related recall issues are more prevalent in children’s clothing.

Sustainability is a widely recognized concern and priority for healthy growth of the society and for preservation of the planet. Concerning this, textile sector has seen an unprecedented demand for sustainable products from the consumers, responding to which organizations have undertaken different initiatives. One of the major concerns in the textile sector is its complex supply chain networks and the involvement of numerous actors dealing with diverse raw materials and operations. The effective implementation of sustainability at the industrial scale would require the participation of all supply chain actors, along with an efficient traceability system to monitor and analyze different sustainability aspects. Furthermore, traceability is an integral part of the recycling process which contributes towards the sustainability. Therefore, the present article focuses on the contribution of traceable information towards attaining the sustainability in the textile sector. The three pillars of sustainability, namely, ecological, societal, and economic, are discussed for their relation and dependency on the traceability followed by an overview of the challenges in successful implementation of the traceability system, which is anticipated to shape the future research questions.

ABSTRACT

Purpose

The purpose of this article is to study the relationship between macro-scale indicators (social, economic and governance) with textile product recalls in the EU. Here the main focus is given to a systemic approach to understand the problem from a holistic perspective, focusing on the interactions among components rather than focusing only on causes.

Design/methodology/approach

The EU’s recalled textile product data and macroscale indicators used in the study were obtained from multiple sources, namely RAPEX, Transparency International, Eurostat, and The World Bank. The data have been used for the years 2008-2013. Multiple linear regression analysis and p-value statistics were used to scale the impact and statistical significance respectively, of the indicators on the textile product recalls.

Findings

Findings from the study suggest that the textile recall is influenced by governance and social aspects of the EU member states while the economic aspect has negligible statistical significance. Results further suggest that better governance and higher social inequality lead to lesser textile product recalls.

Original/value

This study is first to quantitatively identify of the role of social, governance and economic aspects of the EU member states on their textile-product recalls. The previous qualitative research works have been focused on a particular brand or recall which limit the generalization of their conclusions. Whereas, this paper uses a systemic approach to understand the problem from a holistic perspective, focusing on the interactions among components rather than focusing only on causes.

The purpose of this article is to analyse a business scenario, which combines the apparel rent-and-sell strategies to maximize apparel utilization while reducing the overall demand for new products. The developed game theory models hypothesize aspects including product quality, manufacturing cost, product use-cycles, and renting as alternate to buying to investigate their impact on the supply chain actors. Further, the developed supply chain scenarios have been numerically illustrated and compared against the sell-only business scenario to quantify the impact of product renting.

ABSTRACT

Purpose

The focus of this work-in-progress is to develop a general traceability framework for the textile supply chain. Traceability is of a significant importance for the textile industry, firstly due to multiple actors’ involvement in the production and distribution, and secondly the heterogeneous nature of actors, dealing with diverse materials, including fibre spinning mills, yarn spinning mills, weaving industry and garment manufacturers. Moreover, The textile industry has seen a global shift towards recently industrialized countries; consequently, the offshore buyers have become more dependent on complex supply chains and created more information asymmetry as offshore buyers cannot directly observe the production activities of a distant manufacturer. Resulting consequences of information asymmetry can be seen in terms of counterfeit products, malpractices in production and other social and environmental issues. Moreover, textile market is a volatile market because of rapidly changing trends and consumers’ preferences, therefore inter-actor visibility of production activities is required for synchronous production to meet the market demands. Considering the above-mentioned characteristics and challenges in textile industry, this work targets to develop a traceability framework for improving supply chain visibility and integrate various actors in the textile production supply chain.

Design/methodology/approach

The traceability framework development has been divided into four steps, namely, identification of user requirement of different stakeholders in the textile supply chain, identification of different information points, traceability data modelling, and information exchange model to develop traceability among various stakeholders in the textile production supply chain. In-depth analysis was conducted for the need of traceability from various stakeholders’ perspectives in textile sectors, which include various actors in textile production and distribution, consumers and surveillance/certification authorities. An UML case diagram approach has been followed to define the traceability requirements and UML class diagram approach has been adopted for modelling traceability data.

Findings

The present traceability framework is proposed to handle traceability information and information exchange between various stakeholders in the textile production supply chain, which can not only disseminate the traceability information in the supply chain, but also helpful 704 in case of recall crisis (such as product design fault, harmful chemicals or other related issues) where surveillance authorities can track (forward traceability) and trace (for identifying the source error which resulted recall) the products in the textile supply chain for recalling. Moreover, the traceability information can be used by consumers in order to about the product and raw materials’ history.

Practical implications

Traceability works on the credibility of the organizations, which handle the traceability data. Therefore, in the real implementation, either organizations need to be transparent in terms of traceability data or third party certification/audit is required for ensuring that the traceability information provided by an organization is correct/authentic. Secondly the semantics for information exchange are required to be unified across various actors involved in traceability information storage and exchange.

Original/value

The traceability framework covers perspectives from traceability not only from various actors involved in textile supply chain, but also includes consumers therefore, traceability information is collected by this framework can be utilized from industrial as well as consumer and surveillance perspectives.

This paper outlines the quality inspection strategies in a supplier–buyer supply chain under a customer return policy. This paper primarily focuses on product quality and quality inspection techniques to maximize the actors’ and supply chain’s profits using game theory approach. The supplier–buyer setup is described in terms of textile manufacturer–retailer supply chain where quality inspection is an important aspect and the product return from the customer is generally accepted. Textile manufacturer produces the product, whereas, retailer acts as a reseller who buys the products from the textile manufacturer and sells them to the customers. In this context, the former invests in the product quality whereas the latter invests in the random quality inspection and traceability. The relationships between the textile manufacturer and the retailer are recognized as horizontal and vertical alliances and modeled using non-cooperative and cooperative games. The non-cooperative games are based on the Stackelberg and Nash equilibrium models. Further, bargaining and game change scenarios have been discussed to maximize the profit under different games. To understand the appropriateness of a strategic alliance, a computational study demonstrates textile manufacturer–retailer relation under different game scenarios.

Traceability has recently gained considerable attention in the textile industry. Traceability stands for information sharing about a product including the product history, specification, or location. With the involvement of globally dispersed actors in the textile supply chain, ensuring appropriate product quality with timely supplies is crucial for surviving in this industry with ever increasing competition. Hence it is of paramount importance for a supply chain actor to track every product and trace its history in the supply chain. In this context, this paper presents a framework to implement traceability in the textile supply chain. A system approach has been followed, where firstly the usage requirement of traceability is defined, and then a framework for implementing intra-actor or internal traceability and inter-actor or external traceability is discussed. This article further presents a sequential diagram to demonstrate the interaction and information exchange between the actors in the supply chain, when the traceability information is requested. An example is also illustrated for data storage using a relational database management system and information exchange using XML for the textile weaver. Finally, the article discusses challenges and future studies required to implement traceability in the textile supply chain.

Interpretive Structural Modelling (ISM) is widely employed in production research to study the complex interaction among various factors or elements which define a complex production or supply chain problem. It transforms the poorly articulated mental model of the problem into a visible well-defined relational model using an element-relationship-matrix. Building ISM involves primarily pairwise comparison of factors in rotation i.e. each factor is compared with all remaining factors as input. In general, these relations among the compared pairs are defined in binary levels i.e. the relations are defined in terms of “yes/no”; hence, the interactions are treated equally for all levels of interaction magnitude. Consequently, the interpretation of the results does not capture the intensity of interrelation, which limits the exploitation of the relational model for concrete production/supply chain decision-making. This paper introduces a data-driven algorithm to convert a multi-level pairwise comparison into bi-level groups i.e. groups with weak and strong relations, to incorporate and account for non-binary relations. The bi-level groups are created based on a threshold point in multi-level input that simultaneously maximizes the inter-group variance whereas minimizes the intra-group variance. The application of the proposed approach is demonstrated in context to small-series textile/apparel supply network configuration, in order to show its practical significance in strategic decision-making.

Buckypaper (BP) is a planar film that consists of random network of multiwall carbon nanotubes (MWCNTs) held together by weak van der Waals interactions at tube-tube junctions. Although individual carbon nanotubes (CNTs) possess remarkable electrical properties, the electrical resistance of pristine BP is usually too high for practical applications. However, the electrical resistivity of BP can be attenuated by applying modest compressive stresses. Herein, we report an analytical model for predicting the electrical resistivity of BP under defined level of compressive strain. The predictive piezoresistive model of BP was developed by formulating a direct relationship with the structural parameters, physical and electrical properties of CNTs. The basis of the piezoresistive model relied upon the geometrical probability approach in combination with classical Hertzian contact mechanics and constriction resistance techniques. A comparison has been made between the theoretical and experimental results of electrical resistivity of BPs with varying densities. A reasonably good quantitative agreement was obtained between the theory and experiments. The main source of error was caused by the uncertainty in the measurement of the initial BP thickness. Through theoretical modeling, the initial volume fraction of CNTs was found to be one of the key parameters that modulated the piezoresistive behavior of BP.

Prolonging garment longevity is a well-recognized key strategy to reduce the overall environmental impact in the textile and clothing sector. In this context, change or degradation in esthetic or visual appeal of a garment with usage is an important factor that largely influence its longevity. Therefore, to engineer the garments for a required lifetime or prolong longevity, there is a need for predictive systems that can forecast the trajectory of visual degradation based on material/structural parameters or use conditions that can guide the practitioners for an optimal design. This paper develops a deep learning based predictive system for washing-induced visual change or degradation of selected garment areas. The study follows a systematic experimental design to generate and capture visual degradation in garment and equivalent fabric samples through 70 cycles in a controlled environment following guideline from relevant washing standards. Further, the generated data is utilized to train conditional Generative Adversarial Network-based deep learning model that learns the degradation pattern and links it to washing cycles and other seam properties. In addition, the predicted results are compared with experimental data using Frechet Inception Distance, to ascertain that the system prediction are visually similar to the experimental data and the prediction quality improves with training process.

Traceability has gained considerable attention to facilitate monitored production, product recall, safety and reverse supply chain activities, in recent years. Traceability in manufacturing and distribution involves the use of tracking tags which are attached to the products; consequently, the products are tracked by recording the identity of attached tracking tags in the supply chain. In this context, this paper introduces a new yarn coding-based tracking tag which is fully integrated into textile for tracking the textile supply chain. The new tracking tag involves the use of special yarns which act as information carrier and basic unit of the tracking tag. An implementation scenario is discussed to use the designed tracking tag to monitor the production and authentication purposes. Real prototypes of the fully integrated coded yarn based textile tags are demonstrated in woven and knitted structures and analysed under the effect of washing treatments to simulate realistic conditions. Further, an image pattern recognition based algorithm has been introduced and analysed to extract the information encoded in the tag using coded yarns.

Absorptive glass mat (AGM) separators are widely used for valve regulated lead acid (VRLA) batteries due to their remarkable fiber and structural characteristics. Discharge performance and recharge effectiveness of VRLA batteries essentially rely on the distribution and saturation levels of the electrolyte within the AGM separator. Herein, we report an analytical model for predicting the wicking characteristics of AGM battery separators under unconfined and confined states. The model of wicking behavior of AGM is based upon Fries and Dreyer's approach that included the effect of gravity component which was neglected in classic Lucas-Washburn's model. In addition, the predictive model of wicking accounted for realistic structural characteristics of AGM via orientation averaging approach. For wicking under confined state, the structural parameters have been updated under defined level of compressive stresses based upon the constitutive equation derived for a planar network of fibers in AGM under transverse loading conditions. A comparison has been made between the theoretical models and experimental results of wicking behavior under unconfined and confined states. Most importantly, the presented work has highlighted the questionable validity of classic Lucas-Washburn model for predicting the wicking characteristics of AGM separator over longer time duration.

Success of tissue engineering relies on the architecture and properties of porous scaffolds. Electrospun nonwoven scaffolds in the form of mats are unique materials due to large surface area to volume ratio, high porosity, versatility in surface functionalities and excellent mechanical properties. Maneuvering the mechanical behavior ofthe electrospun mat is a major challenge both from theoretical and experimental perspectives. Herein, we report a two-dimensional (2D) analytical model of normalized elastic moduli of electrospun mats by formulating a relationship with the governing fiber and structural parameters. The analytical model of normalized mat modulush as also accounted for fiber curvature in the form of sinusoidal curve along with the specimen dimensions considered during the uniaxial tensile test. A comparison has been made between the magnitudes of normalized matmodulus obtained through predictive modeling and the experimental results adapted from the literature. In general, a good agreement has been found between the theoretical and experimental results of normalized moduli ofthe electrospun mats. An interplay of some of the governing parameters has been analyzed through parametric analysis. Through theoretical modeling, the normalized amplitude of fiber crimp via fiber diameter along withthe aspect ratio of specimen dimensions are observed to be the dominant factors responsible for modulating thenormalized mat modulus.

The fashion industry is often criticised for lack of traceability. This paper explores the implementation of traceability in the fashion supply chain using blockchain technology (BT). Using a demonstrative simulation we identify what are the sector-specific requirements, main procedures (i.e. smart-contract rules), and how to use them for BT-based traceability.

Absorptive glass mat (AGM) separators play a key role in enhancing the cycle life of the valve regulated lead acid (VRLA) batteries by maintaining the elastic characteristics under a defined level of compression force with the plates of the electrodes. Inevitably, there are inherent challenges to maintain the required level of compression characteristics of AGM separators during the charge and discharge of the battery. Herein, we report a three-dimensional (3D) analytical model for predicting the compression-recovery behavior of AGM separators by formulating a direct relationship with the constituent fiber and structural parameters. The analytical model of compression-recovery behavior of AGM separators has successfully included the fiber slippage criterion and internal friction losses. The presented work uses, for the first time, 3D data of fiber orientation from X-ray micro-computed tomography, for predicting the compression-recovery behavior of AGM separators. A comparison has been made between the theoretical and experimental results of compression-recovery behavior of AGM samples with defined fiber orientation characteristics. In general, the theory agreed reasonably well with the experimental results of AGM samples in both dry and wet states. Through theoretical modeling, fiber volume fraction was established as one of the key structural parameters that modulates the compression hysteresis of an AGM separator.

Articular cartilage possesses unique structure and composition giving rise to unusual mechanical behavior. Typically, it is a structurally graded material that displays variation in mechanical properties along the depth. In this communication, the geometrical probability approach has been used for predicting the in-plane Poisson’s ratio in the surface and middle zones of articular cartilage. The presented model has formulated a relationship between the Poisson’s ratio and collagen fibril alignment. A comparison has been made between the theoretical and experimental findings of Poisson’s ratio in the surface and middle zones of human patella cartilage, as obtained from the literature.

Fiber networks with out-of-plane auxetic behavior have been sporadically investigated. One of the major challenges is to design such materials with giant negative Poisson’s ratio over large deformations. Here in, we report a systematic investigation to create three-dimensional (3D) fiber networks in the form of needlepunched nonwoven materials with out-of-plane auxetic behavior over large deformations via theoretical modeling and extensive set of experiments. The experimental matrix has encapsulated the key parameters of the needlepunching nonwoven process. Under uniaxial tensile loading, the anisotropy coupled with local fiber densification in networks has yielded large negative Poisson’s ratio (up to −5.7) specifically in the preferential direction. The in-plane and out-of-plane Poisson’s ratios of fiber networks have been predicted and, subsequently, compared with the experimental results. Fiber orientation was found to be a core parameter that modulated the in-plane Poisson’s ratio of fiber networks. A parametric analysis has revealed the interplay between the anisotropy of the fiber network and the out-of-plane Poisson’s ratio based upon constant volume consideration.

In the last several decades, textile-reinforced composites have emerged as a unique class of materials offering intricate features, reduced fabrication costs, introduced multiaxial reinforcement, and enhanced damaged tolerance. Despite these benefits, textile-reinforced composites face challenges as predicting their performance often relies on heuristics and past experiences without gaining insights into the underlying structure of the textile material and its constituents. This tutorial-based mini-review aims to delve into the fundamentals of textile architecture in the context of textile-reinforced composites and provide an overview of their significant physical and structural features that influence the performance characteristics of textile-reinforced composites. 

Autohesion is a unique class of adhesion that enables the bonding of two identical surfaces by establishing intimate contact at interfaces. Creating intimacy between two identical surfaces poses a challenging task, often constrained by the presence of surface roughness and chemical heterogeneity. To surmount this challenge, we document a variety of autohesive traits in polypropylene-based meltblown nonwovens, accomplished through a facile, scalable, energy-efficient, and cost-effective ultrasonic bonding process. The mean work of autohesion for a single polypropylene bond, serving as a figure of merit, has been computed by extending the classical Johnson−Kendall−Roberts (JKR) theory by factoring in peel strength along with key fiber and structural parameters of nonwoven materials. Achieving a high figure of merit in ultrasonically bonded nonwovens hinges on the synergistic interplay of key process parameters, including static force, power, and welding speed, with the fiber and structural properties acting in concert. In this regard, peel-off force analysis has also been conducted on a series of twenty-seven ultrasonically bonded meltblown nonwovens prepared using a 33 full factorial design by systematically varying process parameters (static force, power, and welding speed) across three levels and extension rate. X-ray microcomputed tomography (microCT) analysis has been performed on select ultrasonically bonded nonwoven samples to discern their bulk characteristics. A broad spectrum of mean work of autohesion for a single polypropylene bond, ranging from 1.88 to 9.93 J/m², has been ascertained by modulating key process parameters.

Rendering any surface non-wettable requires it to be clean and dry after the droplet is deposited or impacted. Leveraging and quantifying the non-periodic or random topology non-wettable is intricate as the Cassie-Baxter state competes with the Wenzel or impaled state, which becomes further challenging for irregular and heterogeneous nonwoven materials. Herein, we report the fundamental insights of the impalement dynamics of droplets on metastable nonwovens and self-similar nonwoven-titanate nanostructured materials (SS-Ti-NMs) using laser scanning confocal microscopy in three dimensions. Our results represent the first example of liquid imbibition in metastable nonwovens and SS-Ti-NMs involving a complex interplay between a triumvirate of factors – the number of fibres in the defined cross-sectional area (volume), pore features, and intrinsic wetting properties of the constituent entities. Predictive models of the apparent contact angle and breakthrough pressure for nonwovens and their SS-Ti-NMs have been proposed based on micro- and nano-scale structural parameters. Enabled by X-ray microcomputed tomography analysis, a key set of three-dimensional fibre and structural parameters of nonwovens has been unveiled, which played a vital role in validating the predictive models of apparent contact angles.

The valve regulated lead acid (VRLA) battery is a predominant electrochemical storage system that stores energy in a cheap, reliable and recyclable manner for innumerable applications. The absorptive glass mat (AGM) separator is a key component, which is pivotal for the successful functioning of the VRLA battery. Herein, the intricate three-dimensional (3D) porous structure of AGM separators has been unveiled using X-ray micro-computed tomography (microCT) analysis. X-ray microCT has quantified a variety of fiber and structural parameters including fiber orientation, porosity, tortuosity, pore size distribution, pore interconnectivity and pore volume distribution. A predictive model of hydraulic tortuosity has been developed based upon some of these fiber and structural parameters. Moreover, the pore size distribution extracted via X-ray microCT analysis has served as a benchmark for making a comparison with the existing analytical model of the pore size distribution of AGM separators. Pore size distributions obtained via the existing analytical model and through X-ray microCT analysis are in close agreement.

The wicking ability of the fabric is one of the most important parameters that affect the comfort of clothing. Silk is a natural long-fiber material, and researching the water-absorption properties of natural silk yarn helps in developing textile products with specific performance characteristics, thereby enhancing the competitiveness of textiles in the market. This research aims to analyze the wicking behavior of silk yarn simulating their interlaced conditions when they were woven. To investigate the effect of yarn twist and yarn fineness, 13 different silk yarns were examined in individual and interlaced scenarios. Results showed that both yarn twist and fineness affect the wicking performance, and the permeability and capillary hydraulic diameter were calculated. A comparison of experimental results and best theoretical fits according to Fries and Dryer’s proposed model. On the other hand, a good correlation was observed between the wicking length of single yarns and interlaced yarns. This suggests that the characteristics of single yarn can be potentially used for predicting the wicking behavior of woven textiles, where yarns form interlacements. This study can usher in innovations and enhancements that can benefit future virtual clothing design and real-world wear.