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 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 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. 

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.

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.

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. 

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.

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.

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.