The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly ...focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases "Scopus" and "Web of Science" and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.
A systematic approach is presented for constructing higher-order immersed boundary and ghost fluid methods for CFD in general, and fluid–structure interaction problems in particular. Such methods are ...gaining popularity because they simplify a number of computational issues. These range from gridding the fluid domain, to designing and implementing Eulerian-based algorithms for challenging fluid–structure applications characterized by large structural motions and deformations or topological changes. However, because they typically operate on non body-fitted grids, immersed boundary and ghost fluid methods also complicate other issues such as the treatment of wall boundary conditions in general, and fluid–structure transmission conditions in particular. These methods also tend to be at best first-order space-accurate at the immersed interfaces. In some cases, they are also provably inconsistent at these locations. A methodology is presented in this paper for addressing this issue. It is developed for inviscid flows and prescribed structural motions. For the sake of clarity, but without any loss of generality, this methodology is described in one and two dimensions. However, its extensions to flow-induced structural motions and three dimensions are straightforward. The proposed methodology leads to a departure from the current practice of populating ghost fluid values independently from the chosen spatial discretization scheme. Instead, it accounts for the pattern and properties of a preferred higher-order discretization scheme, and attributes ghost values as to preserve the formal order of spatial accuracy of this scheme. It is illustrated in this paper by its application to various finite difference and finite volume methods. Its impact is also demonstrated by one- and two-dimensional numerical experiments that confirm its theoretically proven ability to preserve higher-order spatial accuracy, including in the vicinity of the immersed interfaces.
Textiles release microfibers to the environment during production, use, and at end-of-life disposal. There is a potentially large and growing risk to the environment associated with microfiber ...pollution, which requires protection and sustainable development in the textile and fashion industry. To date, early-stage research efforts, perhaps the most important initial actions to explore more feasible and effective solutions, into microfiber pollution from the perspective of environmental sustainability have been fragmented. In this study, we discuss the sustainability of the textile and fashion industry for economic and social development. The potential sources of microfiber pollution are analyzed from the supply chain of the textile and fashion industry. Additionally, actionable solutions, including a shift in consumer behavior, retailer recycling programs, and government behavior in the development of a sustainable economy and environment protection for textile and fashion industry, are proposed. Finally, we conclude that there is no silver bullet solution to microfiber pollution until now, but a collaborative cross-sector group of related industries conducting comprehensive research to inform a multi-industry approach must form part of the answer.
Robust flight control laws based on back-stepping technology and ADRC method are designed for attitude control of a non-linear aircraft system. First, non-linear aircraft model is introduced and ...converted to standard equation of state. Then, the extended state observer is applied to estimate the unknown variables, the homologous ADRC is designed to ensure the state variables of the CLS to astringe to the reference state. Next, the stability of ESO and ADRC are analyzed and proven theoretically. At last, the effectiveness of this method is illustrated by extensive comparative simulations. The results acquired from simulation attest that ADRC can achieve better control performance than PID and SMC method.
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.
The traditional pattern-making process is very time-consuming and requires professional fashion design knowledge. In order to develop a form-fitting garment to meet customer’s individual needs, ...pattern makers must rely on a “trial and error” procedure until the customer is satisfied. In this paper, we proposed a “what you see is what you get” (WYSIWYG) way to efficiently develop garment patterns. First, a three-dimensional (3D) garment, using an extracted outline from a garment flat or figure, is modeled in a gravitational virtual environment. The modeled garment is then adjusted until it meets design requirements. Next, the adjusted 3D garment model is expanded by smoothing out the folds and wrinkles. Construction curves are drawn on the surface of the expanded 3D garment model according to design requirements. These curves divide the 3D garment model’s surface into many small 3D surfaces. Then, 2D garment patterns are obtained by unfolding these subdivided 3D surfaces. Finally, the flattened 2D patterns are stretched and shrank according to the fabric elasticity. The final patterns can be used for making real garments.
Compared to the current 3D garment pattern-making methods, our proposed method is more robust and well-rounded; not only is the proposed approach versatile towards both tight-fitting and loose-fitting clothing, but also requires no prior knowledge of pattern-making from the user. It also involves garment ease allowance, fabric elasticity, and draping, three factors that had not been previously considered all at once during smart pattern-making procedures, in the designing process.
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•A novel interactive pattern-making technology is proposed in this research.•Our method doesn’t require prior pattern-making knowledge from the user.•Garment ease allowance and fabric elasticity are both involved.•Our method is suitable for both tight and loose garments.
In the clothing industry, garment pattern design serves as a significant middle link between fashion design and manufacturing. With the advent of advanced multimedia techniques, like virtual reality, ...3D modeling, and interactive design, this work has become more intuitive. However, it is still a tremendous knowledge-based work that relied on the experienced patternmakers’ know-how. For enterprises, it will take a long time to cultivate a patternmaker from an abecedarian to an expert. Moreover, while facing fierce competition in the market, enterprises still have to endure the pressures and risks led by the turnover of experienced patternmakers. In this context, we put forward a knowledge-supported garment pattern design approach by learning the experienced patternmakers’ knowledge based on fuzzy logic and artificial neural networks. Based on this approach, we created a knowledge-supported pattern design model, consisting of several sub-models following the garment styles, considering the properties of fabrics and fitting degree. The inputs of the model are the feature body dimensions, while the outputs, namely the pattern parameters, can be generated following the fabric and fitting degree selected. Through performance comparison with other models and the actual fitting test, the results revealed that the present approach was applicable. Our proposed approach can not only support the non-expert patternmakers or abecedarians to make decisions when developing the patterns by reducing the difficulties of patternmaking but help the enterprises to reduce the dependencies on the experts, hence promoting the efficiency and reducing risks.
The quick development of mass customization in the apparel industry leads to an exponential increase of garment size combinations for markers, which induces a heavy and complex workload of marker ...making. In this context, due to the complexity of the problem, the classical marker making methods using the existing commercialized software are less performant in terms of efficiency and accuracy. Therefore, machine learning techniques, usually taken as efficient tools for extracting relevant information from data measured in uncertain and complex scenarios, are considered much simpler and faster. In this study, we apply the methods of multiple linear regression (MLR) and radial basis function neural network (RBF NN) to estimate marker lengths that are used in various garment production modes by considering various sets of garment sizes and different marker types. The experimental results show that the proposed approach leads to a good performance in estimating marker lengths of different types of markers (mixed marker and group marker) with diverse size combinations taken from various sets of garment sizes in both mass production and mass customization conditions.
Nowadays, the demand of small-series production and quick response become more and more important in textile supply chains. To meet the increasing trend of customization in garment production, ...forecast based supply chain model is not suitable any more. Demand-driven garment supply chain is developed and employed more and more. However, there are still many defects in current model for demand-driven supply chains, e.g. long lead time, low efficiency etc. Therefore, in this study we proposed a new collaborative model with central order processing system (COPS) to optimize current demand-driven garment supply chain and improve multiple supply chain performances. Common and important supply chain collaboration strategies, including resource sharing, information sharing, joint-decision making and profit sharing, were merged into this system. Discrete-event simulation technology was utilized to experiment and evaluate the new collaborative model under different conditions based on a real case in France. Multiple key performance indicators (KPIs) were examined for the whole supply chain and also for individual companies. Based on the simulation experiment results, we found that new proposed collaborative model gain improvements in all examined KPIs. New model with COPS performed better under high workload condition than under low workload condition. It can not only increase overall profit level of the whole supply chain but also individual profit level of each company.
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