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In the context of fashion/textile innovations towards Industry 4.0, a variety of digital technologies, such as 3D garment CAD, have been proposed to automate, optimize design and manufacturing processes in the organizations of involved enterprises and supply chains as well as services such as marketing and sales. However, the current digital solutions rarely deal with key elements used in the fashion industry, including professional knowledge, as well as fashion and functional requirements of the customer and their relations with product technical parameters. Especially, product design plays an essential role in the whole fashion supply chain and should be paid more attention to in the process of digitalization and intelligentization of fashion companies. In this context, we originally developed an interactive fashion and garment design system by systematically integrating a number of data-driven services of garment design recommendation, 3D virtual garment fitting visualization, design knowledge base, and design parameters adjustment. This system enables close interactions between the designer, consumer, and manufacturer around the virtual product corresponding to each design solution. In this way, the complexity of the product design process can drastically be reduced by directly integrating the consumer’s perception and professional designer’s knowledge into the garment computer-aided design (CAD) environment. Furthermore, for a specific consumer profile, the related computations (design solution recommendation and design parameters adjustment) are performed by using a number of intelligent algorithms (BIRCH, adaptive Random Forest algorithms, and association mining) and matching with a formalized design knowledge base. The proposed interactive design system has been implemented and then exposed through the REST API, for designing garments meeting the consumer’s personalized fashion requirements by repeatedly running the cycle of design recommendation—virtual garment fitting—online evaluation of designer and consumer—design parameters adjustment—design knowledge base creation, and updating. The effectiveness of the proposed system has been validated through a business case of personalized men’s shirt design.

Product security is one of the major concerns in the textile industry. Every year, fashion brands suffer significant loss due to counterfeit products. Addressing this, the paper introduces a secured tag for traceability and security of textile products. The proposed tag is unclonable, which can be manufactured using conventional screen-printing process. Further, it can be read using a smartphone camera to authenticate the product and trace its history. Consequently, imparting additional functionality to the textile through surface modification. To validate its applicability, the study experimentally investigates the durability and readability of the developed secured tag using three different binders on polyester and cotton textiles substrates. A comparison is presented with an in-depth analysis of surfaces and binders interaction at different stages of the secured tag lifecycle, i.e. before print, after print, after wash and after abrasion cycles. The methodology and findings of the study can also be useful for other manufacturing domains dealing with the printing process.

The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today’s global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement. The traceability coding tag consists of unique features for identification, which links the product with traceability information, plays an important part in the traceability system. In this paper, we describe an innovative technique of product component-based traceability which demonstrates that product’s inherent features—extracted using deep learning—can be used as a traceability signature. This has been demonstrated on textile fabrics, where Faster region-based convolutional neural network (Faster R-CNN) has been introduced with transfer learning to provide a robust end-to-end solution for coded yarn recognition. The experimental results show that the deep learning-based algorithm is promising in coded yarn recognition, which indicates the feasibility for industrial application.

Garment purchasing through the e-shopping platforms has become an important trend for consumers of all parts of the world. More and more e-shopping platforms have proposed recommendation functions to consumers in order to make them to obtain more easily desired products and then increase shopping sales. However, there are two main drawbacks in the existing recommendation systems. First, it systematically lacks feedback processing in these systems. If a consumer is not satisfied with the recommendation result, there is no self-adjustment function. The other drawback is that the existing recommendation systems are mostly closed, without considering the possibility of data and knowledge updating. Considering the above drawbacks, we propose a new recommendation system integrating the following features: 1) automatic adjustment of the knowledge according to the consumers’ feedback, 2) making the system open and adaptive so that the consumer can easily add or replace criteria and data. This proposed recommendation system can effectively help consumers to choose garments on the Internet. Compared with the other systems, the proposed one is more robust and more interpretable owing to its capacity of handling uncertainty.

In this work, different woven fabrics with varying raw materials, fiber types and weave constructions were used for studying plasma treatment under different atmospheric conditions. Surface modification was characterized using wetting and capillarity surface analysis methods. Moreover, a fuzzy sensitivity variation criterion was used to select the most relevant parameters for woven fabrics from experimental data measured on the fabrics and during the plasma process. In fact, the results obtained using this learning data-based fuzzy sensitivity variation criterion could effectively validate those obtained from the physical and chemical knowledge on plasma treatment. According to the results, air permeability, fiber count, weave construction and summit density were identified as the most relevant parameters, in addition to electrical power, treatment speed and fiber nature. This finding indicated that these parameters had an influence on the plasma treatment results.

Textile and clothing industry is one of the oldest manufacturing industries and is a major contributor in the economic growth of developing countries. However, from past few decades, it has been criticised for its opaque, unsecured and untraceable nature of supply chain. Addressing these challenges, the paper proposes a system approach to introduce an item-centric secured traceability concept to monitor and control manufacturing processes and supply chain activities. In order to implement such secured traceability system, the paper describes the process for manufacturing, encoding and validating an innovative two-factor secured tag based on particle randomness that is printed on the surface of textile. Being micro-sized, the particles are easy to read and validate with pattern recognition. Further, as achieved through an uncontrolled manufacturing process, the randomness is unclonable to produce counterfeit tags. Furthermore, a sequence of experimental analyses has been conducted using various simulated scenarios to verify its applicability. A secured tag can be a low-cost and durable substitute for detachable, unsecured identifiers commercially available in the market.

Using their ability to change their color according to an external stimulation, chromic materials can be used to form a color-changing textile. Electrochromism, more particularly, is a colour change phenomenon caused by the application of an electrical potential. A flexible textile electrochromic device composed of four layers is presented. In order to improve the lifetime of this structure, the electrical performances of the electrolyte layer are studied. A method to measure and calculate the resistance variations of the electrolyte applied on a textile cotton substrate is given. Relations between the electrical performances of the electrolyte and the electrochromic effect of the device are also highlighted.

Design for longevity is known as an eco-design opportunity and could help to reduce the environmental footprint of energy-free items. However, extending the lifespan of products is not always desirable and the focus should be on achieving an optimal lifespan. Operationally, recommendations for design for longevity usually refer to durability, repairability, upgradability or emotional attachment. The use of high-quality and robust material is frequently stated, although it is not obvious what high-quality material is. Based on a quality by design approach, this study aims to propose a methodology to design for optimal longevity with a consumer-oriented approach. To do so, it includes data collection of product quality and manufacturing processes and then embeds consumers’ knowledge. These are combined into data analysis to help to highlight relationships and the most appropriate quality contributors. This methodology relies on three-steps: first, a single quality score which includes consumers’ knowledge; secondly, a multi-scale reverse-engineering process; and finally a data analysis using principal component analysis. The originality of such a proposal is that it enables the consumers’ knowledge to be considered in the identification of appropriated quality contributors. The proposed methodology is implemented in the fashion sector as it is said to be the second most polluting one. Moreover, given the huge variety of materials and production processes available in textiles, the selection of the most suitable recommendations to support a longer lifespan is very complex. The presented case study involves 29 T-shirts and reveals the mechanical-related strengths to be the main quality contributors.

Sixty-six commonly used suitings were selected as the experimental samples of the current study. The Kawabata Evaluation System was used to measure the mechanical properties of the samples. Each sample fabric was made into a shoulder-back as a part of a men’s suit. In order to study the appropriateness of the samples for making good shaped men’s suits, which is known as fabric formability, sensory evaluation methods have been applied to obtain panelists’ assessments on the shape of the shoulder-backs. During data analysis, principal component analysis was initially adopted to reduce the complexity of the system by extracting a small number of important mechanical properties. Then, a fuzzy neural network was developed to model the underlying relations between the samples’ formability and their mechanical properties. Finally, a number of testing samples were used to verify the effectiveness of the proposed predictive model.

In online transactions of textile products, fabric hand was thought to be inaccessible to consumers. Recently, much effort has been made to study the feasibility of providing consumers with a real sense of fabric through a virtual experience. The current paper proposes to extract fabric hand information from the perspective of visual perception. Two sensory experiments are conducted according to the standardized sensory evaluation procedures on a set of representative textile fabrics by two trained panels. The first experiment is aimed to measure how much fabric hand can be perceived through fabrics’ visual displays. On the basis of the positive results obtained, the second experiment is carried out to further investigate the interactive mechanism between samples’ visual features and their tactile properties. A novel algorithm based on rough set theory and fuzzy set theory is proposed in order to quantitatively measure relations between different sensory information.