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This study looks at two aspects of ancient Chinese genre painting to interpret the more realistic ancient shopping scenes in the painting of the peddler in spring scenery in the Ming Dynasty, namely, the Ming spring scene and the characters’ costume image features. Through the analysis of the detailed characteristics of the style, structure, pattern, and color of the figure costume in the painting of the peddler, a total of five sets of figure costume for an adult male and four children in the painting of the peddler in the spring scenery of the Ming Dynasty in China were digitally restored using the 3D virtual fitting technology, which clearly showed the characteristics of the figure costume of the peddler and children in the Ming Dynasty (1368–1644). The figure’s costume style in the painting of the salesman restored in the article is Beizi, closed crotch trousers, open crotch trousers, and abdominal circumference (a type of apron from the Song Dynasty. It is made to resemble a short apron and is wrapped around the abdomen). The study of the painting of peddler in the Ming dynasty in this work provides a reference for future study on ancient Chinese genre paintings of peddler and the restoration of the costumes of the figures in the painting.

This research presented a novel method using 3D simulation methods to design customized garments for physically disabled people with scoliosis (PDPS). The proposed method is based on the virtual human model created from 3D scanning, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions. The proposed 3D garment design method is based on the concept of knowledge-based design, using the design knowledge and process already applied to normal body shapes successfully. The characters of the PDPS and the relationship between human body and garment are considered in the prototyping process. As a visualized collaborative design process, the communication between designer and consumer is ensured, permitting to adapt the finished product to disabled people afflicted with severe scoliosis.

This study aims to achieve intelligent monitoring of clothing comfort in motion, providing data and technical assistance for the research of comfort in motion and an efficient design foundation for sportswear comfort optimization. In view of the present situation of smart wear used in clothing comfort research, this paper primarily presents the construction of an acquisition system in motion, including temperature, humidity, and pressure data acquisition device, data pre-processing, data storage, and so on, which was called the ATHPD system for short. With the help of this acquisition system, tights in motion may now be tested for pressure, heat, and humidity for the first time. It can also transmit data wirelessly. Meanwhile, in order to verify the effectiveness of the acquisition system, by comparing the measurement data of the acquisition system (ATHPD system) with the measurement data of existing acquisition equipment (AMI3037 pressure measurement system, DS1923 button temperature and humidity recorder). The findings demonstrate that there is no significant difference between the data collected by the two methods, which proves the reliability of the acquisition system in this paper. The system guarantees the same quantity of temperature, humidity, and pressure data to be gathered at the same time and the consistency of the corresponding time points and also provides relevant data support for the evaluation and prediction model construction of human comfort in motion. [ABSTRACT FROM AUTHOR]

At present, garment e-commerce is developing rapidly, and the number of online shopping has increased significantly. In this context, the intelligent evaluation technology of garment fit is particularly important. Fit is the most basic requirement in the process of human dressing. It is of great significance to study the relationship between garment fit and human movement, garment structure, and fabric properties. In this article, we propose a garment fit evaluation model based on back propagation artificial neural network. This method realizes the evaluation of garment fit without any tryout. The inputs of the model are the anthropometric data, garment pattern and fabric properties, while the output is the prediction result of garment fit (fit or unfit). In order to build and train the model, the input and output data were obtained by experiment. And a total of 284 experimental samples were obtained. Through the real try-on test, the results revealed that this approach can effectively evaluate the fit of garment. It introduces new ideas and methods for the intelligent evaluation of garment fit, and has a certain reference value for the research of intelligent evaluation technology of garment fit. [ABSTRACT FROM AUTHOR]

A neural network structure of Long Short Term Memory (LSTM) is proposed which could be used to predict the temperature and humidity of other key parts from the temperature and humidity data of some parts of the human body when wearing tight sportswear, so as to realize the temperature and humidity data prediction of all key points of the human body. The temperature and humidity of different people wearing tights were collected by DHT sensors. The experimental results show that the LSTM neural network structure proposed has higher prediction accuracy than other algorithms, and the model evaluates the feasibility of temperature and humidity data of tights in a state of motion, which facilitates the study of dynamic thermal and humid comfort and reduces the time cost of analyzing the temperature and humidity distribution and changing the law during human movement. It will effectively promote the study of temperature and humidity changes when people wear sports tights, provide theoretical reference for the study of human skin temperature in the field of sports medicine, and provide practical guidance for the application of human skin temperature changes in sports clothing production, diagnosis and prevention of sports injuries. [ABSTRACT FROM AUTHOR]

The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment's fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer's personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer's needs was obtained by performing the routine of "design–fit evaluation–pattern adjustment–comprehensive evaluation", iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current three-dimensional human models used in the apparel industry are mostly rigid and lack semantic information on body positions and body parts. Therefore, it is difficult for designers to make accurate, fast and effective designs from these models. This paper proposes a new parametric three-dimensional human body model based on key position labeling and optimized body parts segmentation. First, by using experts' professional knowledge, we manually realize accurate human body data measurements as well as their interpretation and classification, and extract relevant human body features. After deep analysis, measured data irrelevant to body shape have been excluded by designers. Furthermore, the relation between body shapes and body features have been modeled. Second, based on this relational model, we label key positions on the corresponding three-dimensional body model obtained by scanning and segmenting the whole three-dimensional human body into semantically interpretable body parts. In this way, two databases have been created, enabling us to identify features of all segmented body parts, whose combination corresponds to the whole body shape. Third, for a specific consumer, his/her personalized three-dimensional human model can be obtained by taking a very few number of body measurements on himself/herself, making an appropriate combination of the identified body parts, and adjusting parameters of all involved body parts. By comparing the proposed labeled and segmented three-dimensional human model and the existing human models through a number of experiments, the proposed model leads to more relevant results with high accuracy and high visual quality related to real human body shapes. [ABSTRACT FROM AUTHOR]

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years. [ABSTRACT FROM AUTHOR]

Focusing on the "human–sport-clothing" system, this paper analyzed the influence of combinations of different tights and sport states on human body parts and overall body comfort from multiple dimensions. However, the motion state and some fabric parameters are non-numerical parameters, which could not be used for model analysis directly. In addition, there are too many numerical fabric parameters whose relationships are complicated, and it is difficult for general models to deal with these relationships, resulting in low accuracy of the comfort prediction model. Moreover, when using the artificial neural network to study comfort, it has some difficulties in expressing comfort and low prediction accuracy. To solve these problems, One-Hot was used to encode non-numerical parameters, and then intelligent algorithms were adopted to deal with these complex fabric parameters. Finally, a comfort prediction model was established in combination with an adaptive fuzzy reasoning system. The results showed that different fabric combinations and motion states had significant effects on local comfort (comfort of specific human body parts) and global comfort (whole body comfort). Moreover, the prediction model with non-numerical parameters has higher accuracy than the model without non-numerical parameters, which indicated that the prediction accuracy of the model had been improved after the introduction of One-Hot coding, so the non-numerical parameters cannot be ignored. The particle swarm optimization algorithm-cuckoo search algorithm-adaptive network-based fuzzy inference system hybrid model was superior to the particle swarm optimization algorithm-adaptive network-based fuzzy inference system and cuckoo search algorithm-adaptive network-based fuzzy inference system model in predicting local comfort and global comfort. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and accuracy of predicting the thermal and moisture comfort of skin-tight clothing (also called skin-tight underwear), principal component analysis(PCA) is used to reduce the dimensions of related variables and eliminate the multicollinearity relationship among variables. Then, the optimized variables are used as the input parameters of the coupled intelligent model of the genetic algorithm (GA) and back propagation (BP) neural network, and the thermal and moisture comfort of different tights (tight tops and tight trousers) under different sports conditions is analysed. At the same time, in order to verify the superiority of the genetic algorithm and BP neural network intelligent model, the prediction results of GA-BP, PCA-BP and BP are compared with this model. The results show that principal component analysis (PCA) improves the accuracy and adaptability of the GA-BP neural network in predicting thermal and humidity comfort. The forecasting effect of the PCA-GA-BP neural network is obviously better than that of the GA-BP, PCA-BP, BP model, which can accurately predict the thermal and moisture comfort of tight-fitting sportswear. The model has better forecasting accuracy and a simpler structure. [ABSTRACT FROM AUTHOR]

Optimal ergonomic design for consumer goods (such as garments and furniture) cannot be perfectly realised because of imprecise interactions between products and human models. In this paper, we propose a new body classification method that integrates human skeleton features, expert experience, manual measurement methods, and statistical analysis (principal component analysis and K-means clustering). Taking the upper body of young males as an example, the proposed method enables the classification of upper bodies into a number of levels at three key body segments (the arm root [seven levels], the shoulder [five levels], and the torso [below the shoulder, eight levels]). From several experiments, we found that the proposed method can lead to more accurate results than the classical classification methods based on three-dimensional (3 D) human model and can provide semantic knowledge of human body shapes. This includes interpretations of the classification results at these three body segments and key feature point positions, as determined by skeleton features and expert experience. Quantitative analysis also demonstrates that the reconstruction errors satisfy the requirements of garment design and production. Practitioner summary The acquisition and classification of anthropometric data constitute the basis of ergonomic design. This paper presents a new method for body classification that leads to more accurate results than classical classification methods (which are based on human body models). We also provide semantic knowledge about the shape of human body. The proposed method can also be extended to 3 D body modelling and to the design of other consumer products, such as furniture, seats, and cars. Abbreviations: PCA: principal component analysis; KMO: Kaiser-Meyer-Olkin; ANOVA: analysis of variance; 3D: three-dimensional; 2D: two-dimensional; ISO: International Standardisation Organisation; BFB: body-feature-based [ABSTRACT FROM AUTHOR]

Featured Application: In this current research work, adapted upper bodices clothing in a virtual reality environment for women with scoliosis was developed based on the 3D model of the thorax which helps to easily determine both anthropometric points and morphological curves. Then, a 2D block pattern was then achieved by flattening the 3D patterns through various methods. Different tests considering the evolution of the pathology were also carried out to predict the different styles of clothes. Additionally, the results of the tests also validated our Geometric model of the garment with 3D ease allowances. The proposed methods could benefit the designer, researchers, and manufacturer working in the field of clothing design for physically disables peoples (and, more specifically, for those with scoliosis). Scoliosis is a deformity of the spine that causes disorders of the rib cage. This makes it difficult to design clothes for affected people without following the shape of the rib cage. This paper presents a new 3D clothing design method based on virtual reality for women with physical disabilities such as scoliosis. The current design method is a variation of the draping techniques commonly used by fashion designers to design clothes on a mannequin or human body. However, the current design process highly considers the skeleton and body scan of the person. The skeleton is used to detect the anthropometric points of the patient, while the body scan allows us to detect the morphological contours at the onset of scoliosis. Thus, both allow us to accurately track the patient's morphology and atypical shape. The position of the morphological contours is indicated by reference marks that are closely associated with the skeleton. This helps to automatically adapt the garment to the evolution of the patient's pathology over time. The process of creating the 3D garment was processed based on the 3D model of the thorax, which helps to easily determine the anthropometric points and the morphological curves. Using this data, the geometrical model of the garment could be created with 3D ease allowances. The 2D block pattern was then obtained by flattening the 3D patterns using flattening methods. Finally, various tests were performed considering the evolution of pathology to predict the future garment. These tests validate our geometrical model of the garment with 3D ease allowances by comparing the results with previous work. [ABSTRACT FROM AUTHOR]

Featured Application: In this research study, a new adaptive model of the thorax for peoples with spinal disabilities was proposed for the application of performance clothing design. The adaptive model was designed from the spine and follows the deformation of the spine to adapt the thorax skeleton according to spinal column deformation using the images from the EOS medical scanner. The developed adaptive thorax model helps to design a basic bodice adapted to the patient's evolving morphology through detecting the anthropometric points from certain bone parts of the skeleton. Peoples with spinal disability face a huge problem in the design and development of ergonomically fitted and comfortable clothing. Various research studies on the design and developments of functional clothing for scoliosis patients consider their morphological shapes. However, developing appropriate models of the complicated and deformed anatomical shape of the patient in 3D digitization technologies makes it possible to design a comfortable and fitted garment. The current paper proposes a method for developing a fully parametric 3D adaptive model of the thorax of a patient suffering from scoliosis. The model is designed from the spine and follows the deformation of the spine to adapt the thorax skeleton according to the temporal evolution of the spinal column deformation. The integration of the model of the thorax, adjusted to the patient's data, enables the chain of acquisition, processing, and global model to be validated. The fit of the model could be improved for the different bones and it is possible to modify the angles of the spine to see the evolution of the disease. The developed model greatly helps to further detect anthropometric points from certain bone parts of the skeleton to design a basic bodice adapted to the patient's evolving morphology. [ABSTRACT FROM AUTHOR]

This paper investigates the influences of woven fabric type, impact locations and number of layers on ballistic impact performances of target panels through trauma dimension and panel surface damage mechanisms for lightweight women ballistic vest design. Three panels with 30, 35 and 40 layers of two-dimensional plain weave and another two panels with 30 and 40 layers of three-dimensional warp interlock fabrics were prepared. The three-dimensional woven fabric was manufactured using automatic Dornier weaving machine, whereas the two-dimensional fabric (with similar p-aramid fibre type (Twaron®)) was received from the Teijin Company. The ballistic tests were carried out according to NIJ Standard-0101.06 Level IIIA. Based on the result, woven fabric construction type, number of layers and target locations were directed an upshot on the trauma measurement values of the tested target panels. For example, 40 layers of two-dimensional plain weave fabric panels show lower trauma measurement values as compared to its counterpart three-dimensional warp interlock fabric panels with similar layer number. Moreover, 40 layers of two-dimensional fabric panels revealed 47% and 39% trauma depth reduction as compared to panels with 30 layers of two-dimensional fabric panel in moulded (target point 1) and non-moulded (target point 6), respectively. Due to higher amount of primary yarn involvement, two-dimensional plain weave fabric panel face higher level of local surface damages but less severe and fibrillated yarns than three-dimensional warp interlock fabrics panels. Moreover, three-dimensional warp interlock fabric panels required higher number of layers compared to two-dimensional plain weave aramid fabrics to halt the projectiles. Similarly, based on the post-mortem analysis of projectile, higher projectile debris deformation was recorded for panels having higher number of layers for both types of fabrics at similar target locations. [ABSTRACT FROM AUTHOR]

Featured Application: The researchers propose a new design technique to develop an adaptive 3D model for a spine affected by scoliosis. We used 3D scanning technologies to create a model accurate enough for various applications in the textile industry. The developed adapted 3D model can integrate with the complete skeleton and body shapes of patients with scoliosis, which should facilitate the process of designing comfortable, and high-performance clothing. Scoliosis is one of the most frequently occurring morphological problems in people. People with such an abnormal curve of the spine face a huge problem, not only from the medical point of view but also from that of garment design and production processes. The current paper brings a novel designing technique to develop an adaptive 3D model of the complicated anatomical shape of the human vertebrae using 3D digitization technologies. Even though it is not as accurate as scanning approaches, it is accurate enough for several applications, including visualization and constructing statistical shape models. Thus, the adaptive model will further integrate with the full skeleton and then to the body shapes of scoliosis patients, not only for an easy garment design process but also to produce comfortable and high-performance garments. The model has also the benefit of adjusting for each tissue to all kinds of spine parameters, such as the height and the angles of the bones and disks (especially the three characteristic planes: sagittal, coronal, and transverse). Thus, the full adaptive skeleton model and later its garment design system, considering the current adaptive vertebrae model for fitted, comfortable and well-performing scoliosis patients garment products, could be developed. [ABSTRACT FROM AUTHOR]

Materials used in the technical application including composite reinforcements and ballistic fabrics should show not only good mechanical performance but also better deformational behaviors. Meanwhile, three dimensional (3D) warp interlock fabrics have been widely employed in such applications to substitute the two dimensional (2D) fabrics because of their enhanced through-the-thickness performance and excellent formability. The deformational behaviors of such 3D warp interlock fabrics have been also influenced by various internal and external parameters. To understand and fill this gap, the current paper investigates the effects of the warp yarn interchange ratios inside the fabric structure on the formability behaviors of dry 3D warp interlock p-aramid fabrics. Four 3D warp interlock architecture types made with different binding and stuffer warp yarn interchange ratios were designed and manufactured. An adapted hydraulic-driven stamping bench along with hemispherical punch was utilized for better forming behavior analysis such as in-plane shear angle and its recovery, material drawing-in and its recovery, deformational depth recovery, and required stamping forces. Based on the investigation of various formability behaviors, the formability of (3D) warp interlock fabrics were greatly influenced by the binding and stuffer warp yarns interchange ratio inside the 3D warp interlock structure. For example, preform 3D-8W-0S exhibited a maximum deformational height recovery percentage of 5.1%, whereas 3D-4W-8S recorded only 0.72%. Preform 3D-8W-4S and 3D-8W-8S revealed 1.45% and 4.35% recovery percentages toward the deformational height at maximum position. Besides, sample 3D-4S-8W revealed the maximum drawing-in recovery percentage of 43.13% and 46.98% in the machine and cross direction, respectively, around the preform peripheral edges. On the contrary, samples with higher binding warp yarns as 3D-8W-0S show the maximum drawing-in recovery percentages values of 31.21% and 34.99% in the machine and cross directions respectively. [ABSTRACT FROM AUTHOR]

The goal of this article is to propose a practical and scientific model to complete the state of the art in the techniques of determining the pressure exerted by the medical compression stocking on the limbs. The proposed 3D model is developed to explain the mechanics of materials and in particular the compression phenomenon by the medical compression stocking on complex surfaces. This research includes three main parts: The first part presents the research field and assesses the dependence of the constraint between the crushing stress and local geometry of the medical compression stocking bottom. Meanwhile, a pragmatic strategy is provided to simplify the model substantially, based on the assumption that the medical compression stocking structure is subject to small movements induced by muscular contractions. The second part focuses on solving the exact 3D model as a generalization of Laplace's laws by dividing the complicated model into several simple sub-models, making it easier to reveal the non-trivial relations connecting the crushing stress, the local radius of curvature of the bottom, the membrane forces and the material constituting the bottom. Considering the fact that the bottom has extremely low thickness, the third part further provides the asymptotic model to obtain the limiting behavior of the 3D model, which shows that the bottom resists essentially by its tangential rigidity. Meanwhile, the influences of the radii ratio of curvature on the crushing stress are discussed and highlighted. The simulation results are also verified by the finite element method, aiming to optimize the design of medical compression stockings and increase their therapeutic effect. [ABSTRACT FROM AUTHOR]

Featured Application: Nowadays, 3D warp interlock fabric has become a promising fabric structure to apply in different applications due to its excellent molding ability. However, its performance toward ballistic impact for different threats must be investigated before application. The current research attempts to optimize the ballistic performances of 3D warp interlock fabrics for the development of seamless women's soft body armor. It was found that the warp yarn compositions inside the fabric structure affected the overall ballistic performances of the 3D warp interlock fabric panel. Samples that comprised nearly appropriate binder and stuffer warp yarn ratio compositions inside the 3D warp interlock fabrics showed better energy absorption and lower BFS values than their counterpart traditional 2D plain fabrics. This paper investigates the effects of warp yarns ratios on the ballistic performances of three-dimensional (3D) warp interlock p-aramid fabrics. Four 3D warp interlock variants with different binding and stuffer warp yarns ratios were designed and developed. Except for warp yarns ratios, similar fabric parameters and manufacturing conditions were considered. Two-dimensional (2D) woven fabric having similar material characteristics and recommended for female seamless soft body armor are also considered for comparisons. Five ballistic panels, one from 2D plain weave fabric and the rest four from the other 3D warp interlock variants were prepared in a non-angled layer alignment and non-stitched but bust-shaped molded form. The ballistic test is carried out according to NIJ (National Institute of Justice) standard-level IIIA. Back Face Signature (BFS) was then modeled and measured to compute both trauma and panels' energy-absorbing capability. The result showed significant ballistic improvement in the 3D warp interlock variant with optimum warp yarns ratios over traditional 2D plain weave fabrics. 3D warp interlock fabric panel made with 66.6% binding and 33.3% stuffer warp yarn ratio revealed both lower BFS depth and higher energy absorbing capacity (%) than other panels made of 2D plain weave and 3D warp interlock fabric variants. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to investigate the increase in the electromagnetic shielding effectiveness (EMSE) of a set of five variants of three-dimensional (3D) warp interlock woven fabrics containing silver multifilament yarns arranged in a 3D orthogonal grid. The EMSE enlargement as a factor of increasing the quantity of the conductive material per unit area was investigated. The quantity of the conductive material per unit area in a 3D woven fabric can be enlarged by increasing either the yarn undulation or the number of conductive yarn systems, while the yarn density and yarn fineness are fixed. Thus, the binding depth of the conductive warp was gradually increased for the first four variants in order to increase the yarn undulation. Alternatively, the conductive weft system was doubled for the last variant with the aim of increasing the quantity of the conductive component. It should be noted that changing the weave structure requires less effort and energy while keeping the same threading of warps in the reed compared to altering the warp density. The EMSE was measured in an anechoic chamber and the shielding was satisfactory for all the variants in the frequency range of 1–6 GHz (19–44 dB). The results revealed that increasing only 7% of the waviness degree of the conductive warps led to 17% EMSE improvement due to increasing of the conductive yarns through the thickness of the variants. Moreover, no upward EMSE was detected for the last variant, despite the fact that the conductive weft system was doubled. [ABSTRACT FROM AUTHOR]

Mouldability, along with other mechanical properties, is a very crucial material parameter in various technical textile applications, from composites to soft body armour products. Moreover, the mouldability and recovery behaviours of the material will be affected by various internal and external paramters before, during and after the forming process. The current research particularly tried to study the effects of blank-holder pressure (BHP) and the number of layers not only on the moulding characterstics but also on the recovery behaviour of plain woven p-aramid fabrics made from a high-performance yarn with a linear density of 930 dTex. Samples with various numbers of layers were arranged in the same orientation for the moulding process. The moulding approach utilised a specific moulding device in a low-speed forming process and a predefined semi-hemispherically shaped punch for all specimens. Various important dry textile material moulding characteristics and, most importantly, the moulding recovery properties, such as warp and weft direction drawing-in recovery, center high-point recovery, shear angle recovery etc. were investigated. [ABSTRACT FROM AUTHOR]

In this paper, a perception-based fabric recommendation system is proposed to help fashion designers to select the most appropriate fabric in the design process, meeting the perception of the target consumer. The proposed methodology is based on the development of an interactive hierarchical structure, decomposing the decision problem into five levels: goal, criteria (consumers' requirements), sub evaluation criteria (fabric properties), rating scale, and alternatives, which ensures the analysis of requirements of consumers and knowledge sharing among designers. The proposed knowledge-based recommendation system includes a collaborative design process, a commonly used sensory evaluation procedure, and a computational model using the Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithms. The Fuzzy AHP method is used to structure the fabric selection problem and determine the relevant weights of the concerned criteria and corresponding components, while the Fuzzy TOPSIS method is used to evaluate the alternative fabrics based on the criteria obtained from the AHP process and to give the total final ranking of the involved fabric alternatives. Experimental results indicate that, using the proposed interactive hierarchical structure, professional knowledge of the designers can be fully extracted to ensure a high level of consumer satisfaction. [ABSTRACT FROM AUTHOR]

Garment opening is strongly related to the daily life quality of physically disabled people with scoliosis (PDPSs). This research proposes a kinesiological evaluation method to investigate the influence of garment opening positions in dressing activities of PDPSs. For this purpose, dressing activities are firstly deconstructed into several motions. The performance of different garment opening positions is represented by the performance of the wearer carrying out different motions in dressing activities. The proposed evaluation method is based on the Completion Level (CL), which includes both physical and psychological indicators: Motion Standard Time (MST), Motion Difficulty Degree (MDD) and Motion Independence Degree (MID). MST is a physical indicator regarding the standard time to complete a motion in the dressing activities. It reflects the physical autonomy of the wearer in the dressing activity. MDD and MID are psychological indicators, which are developed to evaluate the psychological independence and difficulty of dressing motions respectively. Subjective evaluation is used to obtain data regarding MDD and MID. Due to the fact that there is always vagueness and uncertainly in the subjective evaluation, fuzzy set theory is applied to the data process regarding MDD and MID. A case study is presented to explain the implications of the proposed method. The case study indicated that garment opening positions have an influence on the dressing activity. PDPS will benefit from a well-constructed garment, and a proper positioning of the opening, in terms of both physical and psychological aspects. [ABSTRACT FROM AUTHOR]

This research puts forward a novel knowledge-supported design process for obtaining personalized ready-to-wear garment products aimed at consumers with atypical morphology by using a virtual a three-dimensional-to-two-dimensional (3D-to-2D) design method. The proposed design process starts with designing a personalized garment block, which is then extended into the desired ready-to-wear garment style. The garment block is obtained by using a virtual 3D-to-2D design method. The extension and sizing of the garment block pattern into desired ready-to-wear garment patterns is performed in a 2D environment using classic methods. The proposed design solution begins with a personalized garment block, thus avoiding the complicated operations of simulating a 3D garment directly in the virtual environment. The proposed design process can be fully digitalized, which ensures the involvement of the consumer throughout the design. By repeatedly running the sequence of Design – Display – Evaluation – Adjustment, which is technically accomplished through the virtual 3D-to-2D design method and 3D virtual try-on, the designers’ patterns and principles of elaborating personalized garment products can be validated within a very short time. As a knowledge-supported design process, designers’ design ideas and principles of personalized design solution can be fully extracted to enhance satisfaction of the final product. A set of experiments and case studies validated the approach and application of the proposed knowledge-supported virtual design process, and demonstrated its efficiency. [ABSTRACT FROM AUTHOR]