Garment Design Research Articles

Block pattern design system using 3D zoning method on digital environment for fitted garment

The 2D pattern-making methods which are entirely based on body measurements are widely used and practiced to design garments in today’s digital environment. Such a method is highly criticized due to a lack of consideration for the body's different morphological and anthropometric data. However, such a method would be effective with many precise and repetitive adjustments and with the help of manual expert know-how. For the last few decades, designers have also used a garment designing technique that is carried out directly on a physical mannequin or fashion model, called the draping technique. The current study is devising and implementing a digital garment design technique in the 3D pattern-making process to create and fit the human body morphology in a 3D digital environment. This technique is based on the analysis of 2D pattern-making methods used by model makers. It also highlights the morphological contour and anthropometric links that have been used to develop a similar method in a 3D environment. However, the proposed methods are performed directly on the 3D wearer's morphology as in the manual 3D draping techniques, which avoids morphological interpretation errors. Such a process helps to control the fit of the 3D garment by determining the ease allowance. Besides, the methods integrated a 3D zoning technique to improve the 3D modelling and to ensure the best possible drape and analysis between the garment and the wearer's body. Further work is required to validate the block pattern for general application based on various parameters including fabric, garment style, body position, etc.

Fashion and game design as hybrid practices: approaches in education to creating fashion-related experiences in digital worlds

ABSTRACT Collaborations between fashion and digital game design practitioners are intended to provide new fashion-related experiences in digital worlds, yet often result in garment designs that resemble physical equivalents rather than constituting innovative experiences. This research aimed to investigate possible criteria for developing digital design practices in higher education in fashion design that are informed by industry experts’ experiences of fashion and digital game design. Specialists from both industries were interviewed to investigate how fashion design practices can create meaningful content for digital worlds. The findings suggest that fashion design practitioners in higher-education need to better understand the technical and socio-dynamic peculiarities of digital worlds to create meaningful fashion-related outcomes, rather than recreating physical fashion in the digital realm. The findings further suggest that fashion designers would benefit from learning about digital software, tools, and methods that are shared by digital-native design disciplines to allow for connected workflows.

Application of Multipopulation Genetic Algorithm in Industrial Special Clothing Design

Industrial special clothing refers to the clothing worn by employees of industrial enterprises and departments in order to dress uniformly, facilitate work, or protect their bodies from external injuries. They have the common characteristics of practicality and identification. This kind of clothing includes many styles, such as uniforms, shirts, jackets, overalls, skirts, aprons, and headscarves, which are also applicable in different fields. For example, the clothing for high-temperature operation requires high temperature resistance and comfort, while the clothing for high-altitude operation requires high clothing strength and can play a protective role. General interactive genetic algorithm only supports single user evaluation. The results can only reflect the preferences of a single user. The product designed by interactive genetic algorithm needs to meet the needs of as many users as possible, and a multiuser interactive genetic algorithm suitable for population design is proposed. The detailed design of three main modules of the algorithm, namely, population initialization module, single population module, and multipopulation module, is given. Finally, the algorithm and the general single user interactive genetic algorithm are applied to the garment design system for comparative experiments. The effectiveness of the algorithm in group design is verified.

Application of sustainable power and laser washing device in garment design

Abstract With social progress, economic development, the rapid improvement of science, technology and productivity, and the profound change of social productivity, there has been a serious energy and environmental crisis all over the world. By using the candle burning effect of laser, the laser cleaning device can directly absorb the laser energy, vaporize and decompose the pollutants on the surface of clothing materials, so as to achieve the purpose of cleaning. Energy is an important material basis for human survival and development. Stable, reliable and safe energy supply system and efficient, economic and clean energy utilization are indispensable. The prediction of the development trend of solar power generation technology shows that the trend of optical fiber power generation is clear. The combination of sustainable power and laser water washing device is applied to the garment cleaning process. It analyzes that the garment materials are affected by the laser cleaning speed and laser cleaning power in the cleaning process, and adjusts the appropriate parameters to achieve effective cleaning. The application of sustainable power generated by photovoltaic power generation to the actual process of clothing can not only reduce the consumption of natural resources, environmental pollution and other problems, but also be applied to clothing and other manufacturing industries without restrictions. In the era of sustainable development, energy plays an important role in the coordinated development of economy, society and environment. When the carbon fiber surface is cleaned by laser, the relationship between cleaning spacing, cleaning speed, cleaning area and cleaning time is conducive to formulate a reasonable cleaning scheme. According to the experimental research, when the power P = 100 W, the carbon fiber surface is cleaned of particulate matter, and the laser-cleaned surface is in better condition than when it is treated at high temperatures.

Evaluation of Biophysical Properties of Potential Materials for the Manufacture of Protective Garments for Preterm Infants

Preterm infants, due to immature and dysfunctional skin, have increased water loss through the skin and consequently a decreased body temperature. In order to develop protective garments for preterm infants, it is important to select materials that will protect the child against water and heat loss. The authors are currently involved in the development of protective garments for premature babies, which are similar to baby clothes and contain a membrane that is partially permeable for vapor in combination with textile materials. This article presents the study of materials intended for the production of protective garments for pre-term infants. Samples of materials were investigated to determine biophysical comfort (tests of heat resistance, vapor resistance according to PN-EN ISO 11092:2014-11 and air permeability according to PN-EN ISO 9237) and porosity, surface mass in accordance with PN-EN 12127, and thickness in accordance with PN-EN ISO 5084. In order to determine the porosity of materials and to visualize the structure, tests on computer microtomography were carried out. The mechanical properties of the tested materials and the evaluation of the total hand value were characterized; the samples were tested on the KES device. The aim of this study was to select the most suitable fabrics for protective garments for premature infants to prevent excessive heat and moisture loss from the body, which can lead to hypothermia. For laminates, the optimal results of vapor resistance and heat resistance were obtained for laminate (15 g·m−2 PE foil + 15 g·m−2 PP non-woven), with a level of thermal resistance of 0.0766 m2·K·W−1 and vapor resistance of 188.729 m2·Pa·W−1, and for laminate (15 g·m−2 PE foil + 10 g·m−2 PP non-woven), with a level of thermal resistance of 0.0683 m2·K·W−1 and vapor resistance of 164.085 m2·Pa·W−1. For knitted fabrics, knitwear single cotton 155 g·m−2 showed the highest thermal resistance (0.0296 m2·K·W−1), and knitwear interlock polyester 120 g·m−2 showed the lowest thermal resistance (0.0179 m2·K·W−1). Knitwear cotton 120 g·m−2 had the highest water vapor resistance (8.402 m2·Pa·W−1), while knitwear interlock polyester 130 g·m−2 sample had the lowest resistance (6.356 m2·Pa·W−1). Garments for premature babies should have moisture barrier properties and high thermal insulation. They should also be characterized by optimal air permeability properties. Sample two-layer laminate (15 g·m−2 PE foil + 15 g·m−2 PP non-woven) had the best vapor resistance and thermal insulation properties. Moreover, this sample was characterized by good air permeability and surface weight compared to the other laminate samples. During the design of garments for premature babies, it is important to reduce the surface weight to as low as possible. Among the knitted fabrics, a knitwear single cotton 120 g·m−2 knitwear polyester interlock 120 g·m−2 was selected for having the best THV or tactile comfort. In addition, these knits were chosen for their lower surface weight. Based on the conducted tests, two-layer laminate (15 g·m−2 PE foil + 15 g·m−2 PP non-woven), the knitwear single cotton 120 g·m−2, and knitwear polyester interlock 120 g·m−2 were selected for further research.

NeuralTailor

The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment shape from deformations due to fabric properties, physical forces, and contact with the body. We propose to use a garment sewing pattern, a realistic and compact garment descriptor, to facilitate the intrinsic garment shape estimation. Another major challenge is a high diversity of shapes and designs in the domain. The most common approach for Deep Learning on 3D garments is to build specialized models for individual garments or garment types. We argue that building a unified model for various garment designs has the benefit of generalization to novel garment types, hence covering a larger design domain than individual models would. We introduce NeuralTailor, a novel architecture based on point-level attention for set regression with variable cardinality, and apply it to the task of reconstructing 2D garment sewing patterns from the 3D point cloud garment models. Our experiments show that NeuralTailor successfully reconstructs sewing patterns and generalizes to garment types with pattern topologies unseen during training.

True seams

Seams play a fundamental role in the way a garment looks, fits, feels and behaves. Seams can have very different shapes and mechanical properties depending on how fabric is overlapped, folded and stitched together, with garment designers often choosing specific seam and stitch type combinations depending on the appearance and behavior they want for the garment. Yet, virtually all 3D CAD tools for fashion and visual effects ignore most of the visual and mechanical complexity of seams, and just treat them as joint edges, their simplest possible form, drastically limiting the fidelity of digital garments. In this paper, we present a method that models seams following their true, real-life construction. Each seam brings together and overlaps the fabric pieces to be sewn, folds the fabric according to the type of seam, and stitches the resulting assembly following the type of stitch. To avoid dealing with the complexities of folding in 3D space, we cast the problem into a sequence of simpler 2D problems where we can easily shape the seam and produce a result free of self-intersections, before lifting the folded geometry back to 3D space. We run a series of constrained optimizations to enforce spatial properties in these 2D settings, allowing us to treat asymmetric seams, gatherings and overlapping construction orders. Using a variety of common seams and stitches, we show how our approach substantially improves the visual appearance of full garments, for a better and more predictive digital replica.

Smart Contract for Digital Garment Design using Blockchain and Digital Right Management

Background: In the current advancement of communication, anyone can create digital content. Emerging digital marketplaces provides an environment to share digital data with customers who are interested in content, from other digital sources. Digital marketplaces provide a location for both providers and consumers to connect to meet the increased demand. All of this leads to challenges in content protection, copyright protection, contract creation and work trading. Objectives: To address these issues, this scheme suggests a blockchain based approach for managing digital rights to garment design works. This system uses the blockchain, digital copyright management techniques and off-chain computation for garment design work. The visibility of design effects, the secrecy of design details and compliance with applicable regulations are all considered. The data delivery is also assured with the help of the proof-of-delivery concept. Methods: The proposed system has three steps. They are (i) Creating digital copyright documents using key generation and verification of digital signature (ii) Smart contract creation (iii) Certificate generation for confirmed smart contracts. A Smart contract is defined as computerized transaction protocols that execute the terms of a contract. Findings: Smart contracts are created between the designer and customer using Ethereum which is a blockchain based software platform. The interplanetary file system is used to store digital documents. Ethereum blockchain is used to create the smart contract digitally. Ethereum smart contract provides unchangeable, transparent, tamper-proof logs, traceability and responsibility. Finally, E-certificate is generated by the designer for the confirmed contracts and it is uploaded into the IPFS. Novelty and applications: The trusted, decentralized and proof of delivery frameworks are included for digital design work with the key features of IPFS, blockchain and Ethereum smart contract. The proposed work is also compared with the existing works based on several criteria such as blockchain, IPFS, PoD, etc. Keywords: Blockchain; Design work; Copyright Protection; Digital Signature; Ethereum

Move With the Theremin: Body Posture and Gesture Recognition Using the Theremin in Loose-Garment With Embedded Textile Cables as Antennas

We present a novel intelligent garment design approach for body posture/gesture detection in the form of a loose-fitting blazer prototype, “the MoCaBlazer.” The design is realized by leveraging conductive textile antennas with the capacitive sensing modality, supported by an open-source electronic theremin system (OpenTheremin). The use of soft textile antennas as the sensing element allows flexible garment design and seamless tech-garment integration for the specific structure of different clothes. Our novel approach is evaluated through two experiments involving defined movements (20 arm/torso gestures and eight dance movements). In cross-validation, the classification model yields up to 97.18% average accuracy and 92% f1-score, respectively. We have also explored real-time inference enabled by a radio frequency identification (RFID) synchronization method, yielding an f1-score of 82%. Our approach opens a new paradigm for designing motion-aware smart garments with soft conductive textiles beyond traditional approaches that rely on tight-fitting flexible sensors or rigid motion sensor accessories.

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm.

With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.

Numerical Prediction of the Heat Transfer in Air Gap of Different Garment Models

Abstract This study aims to investigate the characteristics of heat transfer and its mechanism for styling a design garment differently, and to improve thermal comfort caused by clothing styling design, a computational fluid dynamics (CFD) approach has been used to perform numerical investigations of fluid flow and heat transfer across a clothing air gap. Relationships between the heat transfer from the body to clothing (computed by heat transfer equations) and the air gap were examined by body heat loss of different styles of garments. Also, the clothing temperature distribution of different garments was obtained and compared. Computed results reveal that the air gap can play a central role in the heat transfer from the body to the surface of different style garments. When the air gap is small enough, namely about 5 mm in the chest and bust of the X-type of clothing, the conductive heat flux can transfer through the air gap and reach the cloth surface easily, which will bring about the increase of temperature on the clothing surface. The decreasing air gap distance from 50 mm (O-type) to 10 mm (X-type) increases the convective heat flux by up to 25% on the waist. However, the airspeed will increase to greater numbers while the air gap decreases to narrow channels, and it will bring about fierce forced convection heat flux. So the heat transfer must be considered in the process of garment design, and the air gap should be kept at a suitable level. These findings can be used to improve the clothing’s thermal comfort or optimize the cloth structure.

An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques

This paper presents a garment design recommendation system based on two mathematical models that permit the prediction and control of garment styles and structural parameters from a consumer’s personalized requirements in terms of fitting and aesthetics. Based on a formalized professional garment knowledge base, enabling the quantitative characterization of the relations between consumer profiles and garment profiles (colors, fabrics, styles, and garment fit), these two models aim at recommending the most relevant garment profile from a specific consumer profile, using reasoning with fuzzy rules and self-adjusting the garment patterns according to the feedback of the 3D virtual fitting effects corresponding to the recommended garment profile, using a genetic algorithm (GA) and support vector regression. Based on these knowledge-based models, the proposed interactive recommendation system enables the progressive optimization of the design solution through a series of human–machine interactions, i.e., the repeated execution of the cycle “design generation—virtual garment demonstration—user’s evaluation—adjustment” until the satisfaction of the end user (consumer or designer). The effectiveness of this interactive recommendation system was validated by a real case of pants customization. In a manner different from the existing approaches, the proposed system will enable designers to rapidly, accurately, intelligently, and automatically generate the optimal design solution, which is relevant in dealing with mass customization and e-shopping for fashion companies.

Computational Design of Kinesthetic Garments

AbstractKinesthetic garments provide physical feedback on body posture and motion through tailored distributions of reinforced material. Their ability to selectively stiffen a garment's response to specific motions makes them appealing for rehabilitation, sports, robotics, and many other application fields. However, finding designs that distribute a given amount of reinforcement material to maximally stiffen the response to specified motions is a challenging problem. In this work, we propose an optimization‐driven approach for automated design of reinforcement patterns for kinesthetic garments. Our main contribution is to cast this design task as an on‐body topology optimization problem. Our method allows designers to explore a continuous range of designs corresponding to various amounts of reinforcement coverage. Our model captures both tight contact and lift‐off separation between cloth and body. We demonstrate our method on a variety of reinforcement design problems for different body sites and motions. Optimal designs lead to a two‐ to threefold improvement in performance in terms of energy density. A set of manufactured designs were consistently rated as providing more resistance than baselines in a comparative user study.

Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes

Although the measurement of human sizes and the reconstruction of mannequins have been extensively studied, there has been little research on systems applicable for measurement and reconstruction under clothes. This work presents a method for three-dimensional (3D) mannequin reconstruction and human body size measurement based on information fusion of multiple sensors. For this purpose, a multi-sensor information acquisition system (MIAS) is developed allowing one to acquire 11 key size data of the human body under clothes. The MIAS is based on the proposed parametric mannequin method and the Laplace mesh deformation technique, thus allowing one to reconstruct the mannequin conforming to the MIAS measurements. All mannequins reconstructed by this method have the same topology, which will greatly facilitate subsequent processing, such as garment design, customization and virtual fitting. The results of the reconstruction experiments show that our system can reconstruct a mannequin that matches the size of the feature parts very well. In addition, performed comparison experiments with manual and laser measurements shows that the measurement results of our system are close to those of manual and laser measurements. This provides a viable method for measuring key body sizes under clothes and reconstructing the corresponding 3D mannequin.

Research on the Flattening Method of an Energy-Based Fabric Deformation Model in Garment Design

Surface flattening plays an important role in the whole process of garment design. We proposed a novel method by using three-dimensional triangle mesh flattening in this study. First, the three-dimensional triangle mesh is flattened to a two-dimensional plane to approximate the original surface. The initial flattening results are then used as preliminary guesses for subsequent optimizations. Considering that the deformation energy in the real woven fabric is related to tensile or shear deformation, a simplified fabric deformation model based on energy is proposed to update the energy distribution to determine the best two-dimensional pattern. An innovative unified axis system process is proposed to obtain the deformation energy, and energy relaxation in local flattening is proposed to release the distortion of flattening. Finally, the experimental results show that complex surfaces such as garments could achieve better flattening results. Compared with other energy-based methods in garment design, our proposed methods are more flexible and practical.

The assessment of a novel lower body resistance garment as a mechanism to increase the training stimulus during running: a randomised cross-over study

BackgroundThis study examined the physiological and perceived impact of wearing a novel lower body resistance garment during exercise and recovery.MethodsUsing a randomised cross-over design, 15 recreationally-active males performed 2 × 10-min steady-state runs followed by a 10-min passive recovery with concomitant monitoring of oxygen consumption (V̇O2), heart rate (HR) and rating of perceived exertion (RPE; exercise portion only), wearing either the resistance garment (experimental) or running shorts (control).ResultsDuring exercise, there was a trend for V̇O2 and RPE to be higher (4.5% and 7.7% respectively) in experimental than control (V̇O2: r = 0.24, p > 0.05; RPE: r = 0.32, p > 0.05) and for HR to be lower (− 0.4%, r = − 0.05, p > 0.05). During recovery, V̇O2 and HR tended to be lower (4.7% and 4.3% respectively) in experimental than control (V̇O2: r = − 0.32, p > 0.05; HR: r = − 0.27, p > 0.05).ConclusionsThough effects were trivial to small, and not statistically significant, these findings provide proof of concept and suggest that this garment design may increase the training stimulus during running and aid post-exercise recovery.

Fabrics Recommendation for Fashion Design by Using Fuzzy Logic and Rough Sets

Background: Fabric is one of the key and vital design factors in fashion design. However, selection of relevant fabrics is rather complex for designers and managers due to the complexity of criteria at different levels. Introduction: In this paper, we propose a new fabric recommendation model in order to quickly realize fabric selection from non-technical fashion features only and predict fashion features from any fabric technical parameters. This approach is extremely significant for fashion designers who do not completely master fabric technical details. It is also very useful for fabric developers who have no knowledge on fashion markets and fashion consumers. Method: The proposed fabric recommendation model has been built by exploiting designers’ professional knowledge and consumers’ preferences. Concretely, we first use fuzzy sets for formalizing and interpreting measured technical parameters and linguistic sensory properties of fabrics and then model the relation between the technical parameters and sensory properties by using rough sets. Next, we model the relation between fashion themes and sensory properties using fuzzy relations. By combining these two models, we establish a hybrid model characterizing the relation between fashion themes and technical parameters. Result: The proposed model has been validated through a real fabric recommendation case for designer’s specific requirements. We can find that the proposed model is efficient since the averaged value of prediction errors is 8.57%, which does not exceed 10% (generally considered as allowable range of human perception error). Conclusion: The proposed model will constitute one important component for establishing an intelligent recommender system for garment design, enabling to support innovations in textile/apparel industry in terms of mass customization and e-shopping.

Virtual Garment Piece Design and Stitching Algorithm Based on Virtual Simulation Technology

Modern computer-aided design (CAD) technology is widely used in the field of fashion design. Designers do not have to draw again every time they carry out template design, but can draw and change graphic design drawings directly on the computer using CAD technology. In this way, the designer can see the fitting effect of two-dimensional clothing on the three-dimensional human body in real time in the process of garment graphic design, and the three-dimensional fitting results can change in real time according to the change in two-dimensional clothing pieces. Then, the efficiency of garment design will be greatly improved, and the labor cost and resource time cost will be greatly reduced, and if the cloth of fitting can be highly simulated, the virtual fitting will have more real effect and higher value. Virtual clothing design has always played an important role in the clothing manufacturing industry. In recent years, there are more and more application scenarios of computer-aided design of clothing, and it has good engineering application value to study and realize a reasonable and efficient virtual sewing algorithm for clothing pieces. At home and abroad, the research on 3D clothing fitting technology has gradually deepened, and a series of research results have been achieved in the aspects of clothing piece modeling, clothing piece stitching, and collision detection. However, the traditional collision detection algorithm has an infinite loop in the establishment of the bounding box. At the same time, the time complexity of the collision detection algorithm is high. Based on the “personalized virtual clothing design system platform” independently developed by the research group, this study realizes the interactive design function of clothing pieces and researches and realizes the sewing and folding algorithm of clothing pieces. The clothing piece is triangulated, the generated triangular mesh is used to simulate clothing, finite element analysis is used to simulate the physical movement of clothing, and distance field technology is used to simulate the collision effect between the 3D human model and the clothing piece. To achieve the realistic folding effect of 3D virtual clothes, a seam folding algorithm is suggested, which puts extra bending force limits on patches near the seam, and it works well in the test environment. The stress state of the garment is recreated by assessing the internal and exterior pressures on the fabric. Second, using three-dimensional virtual stitching technology, the two-dimensional clothing is turned into a three-dimensional garment. Finally, the bounding box tree for the clothing and the human body is built. Finally, the bounding box detection algorithm detects and handles the collision between the fabric and the human body during sewing. Finally, texture mapping between fabric and bitmap is used to approximate the garment’s true texture effect. Finally, by investigating the application process of wind force on the fabric, the dynamic impact of cloth under wind is simulated. At present, this technology can simulate the dressing effect of conventional clothing categories, and it can obtain ideal dynamic simulation effect of cloth under the condition of wind blowing.

Design of Knitted Garment Design Model Based on Mathematical Image Theory

Iconology is an approach used by Western art theorists and art history researchers to explain plastic art and even relative to other various plastic art activities. This article is aimed at researching and discussing the design of knitted garment design model based on mathematical image theory. This article first discusses the application of digital image processing in the field of knitted clothing; digital image processing technology has a wide range of applications in the field of knitted clothing, including four aspects of fiber, yarn, fabric, and clothing, and proposes digital image filtering technology and digital image fuzzy filtering algorithm and then researches and analyzes the drape performance of knitted fabrics. Fabric drape is an important performance that reflects the beauty of the appearance of the fabric. The design of knitted garments is discussed based on mathematical imagery, and finally, the pressure performance of knitted garments is tested and analyzed. The experimental results in this paper show that the data correlation of the clothing pressure measured in different situations of the upper body is 0.87328, 0.779832, and 0.780213, respectively, under the plain weave, rib, and mesh fabric clothing fabrics. It shows that under the pressure of this degree of knitted clothing, it is in line with the needs of human healthy growth, because a certain degree of clothing pressure can maintain human physical and mental health and improve the quality of life.

Recycling technologies for enabling sustainability transitions of the fashion industry: status quo and avenues for increasing post-consumer waste recycling

A major environmental issue of the rise in garment production is a steep increase of textile waste. At the same time, recycling technologies appear to be promising avenues for sustainability transitions of the fashion industry. This article examines the chances and challenges of scaling recycling technologies for textiles and considers the collection and sorting processes as well as the actual reuse. Drawing on case studies and expert interviews, the results indicate current obstacles and opportunities for accelerating diffusion of recycling technologies. Various initiatives are promising, however industry structures, garment designs, and business models need to be re-orientated and re-aligned to accelerate these recycling concepts. Applying the greenwashing notion that acknowledges co-creation by producers and consumers, the article critically argues that the focus on recycled polyester bottles for garments distorts incentives to improve other recycling technologies and masks the true cost of textile-waste recycling. Instead, fashion-brand companies and retailers should leverage their market power to foster sustainability standards in the global recycling industry.