Water transport through garments has influence on the microclimate between the garments and the body beneath; thus the thermal comfort feeling for the wearer. Soybean protein fiber (SPF), a new type environmental fiber, which has been reported to be superior in water transfer, is often blended with cotton to improve the water transport property. In this paper, T-shirts made of this SPF/cotton blended fabric were focused in comparison with T-shirts made of cotton fabric. Wicking and immersion tests were carried out on the two types of fabrics to investigate the water transport and absorption properties, respectively; wear trials of T-shirts made of the fabrics were also conducted. Comparing with the cotton fabric which had better water absorptive property, it was found that the blended fabric with superior wicking ability could not only delay the increase of the vapor pressure under the T-shirt at the beginning of the exercise, but also help to keep it lower through the exercise significantly, and also kept the skin temperature under the T-shirt lower. It was made clear that it is the water transfer property rather than the water absorption property helps to take away sweat quickly and prevents the increase of the humidity and temperature at skin surface, thus maintaining a comfort microclimate under garments.

The goal of this study was to develop an approach that could automatically generate the customized patterns for women’s suits based on the body measurements taken from two-dimensional (2D) frontal and side images of a subject. The 26 important pattern dimensions relevant to certain body dimensions were first chosen, and the mapping relationships between the body and pattern dimensions were then established for pattern alterations. For the body dimensions (e.g. girths) that could not be directly measured in the 2D images, prediction models were created based on the available width and depth measurements. The body measurements from the 2D images (auto-measurements) of 295 subjects were compared with the corresponding manual measurements, which showed a good correlation between the auto and manual measurements. The try-on test of five suits made with the altered patterns demonstrated the good fitting effects of the customized suits at important characteristic landmarks of five participating subjects through a visual evaluation. The subjective test also showed a satisfactory result of clothing fit under five different postures. Since this pattern-making method is originated from the relationship between the features of a human body and the elements of a pattern prototype, the generated patterns are individualized by unique body shapes to attain a good fit. This method can also accelerate the pattern-making process, reducing human efforts, costs, and production time.

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Purpose -The purpose of this paper is to focus on the determination of distance ease of pants from the 3D scanning data of a clothed and unclothed body. Design/methodology/approach -A human model whose body size conformed to the Chinese dummy standard and four pairs of suit pants were chosen for the study. The scanned surfaces of both the body and the pant were superimposed based on the preset markers. The circumferences at four important positionsabdomen, hip, thigh and kneewere selected for pant ease determination. At one position (e.g. hip), the two cross-sections were divided into several characteristic sections and the distance ease, i.e. the space between the cross-sections at each section was measured. The regression equations between the distance ease and ease allowance were then derived so that the distance ease can be estimated. Findings -The relationship was found between the distance ease and the ease allowance. Meanwhile, a mathematic model was established to convert the distance ease into the increments of a pant pattern, which helps to develop an individual pant pattern automatically. Social implications -The paper provided the concept and the method to customize a pant by using the 3D scanning data of body. It created a link between the 3D distance ease and the 2D ease allowance, and the model to calculate the distance ease increments which warrant proper ease distributions. The method helps to develop an individualized garment pattern automatically from a basic and tight pant pattern. Originality/value -Understanding the relationship between the distance ease and the ease allowance and increments of pattern could help develop an individual apparel pattern from 3D measurements. This paper showed a way to solve the problem of distribution of the apparel ease in a virtual environment and convert body measurements from a 3D scanner into personalized apparel patterns.

Purpose The goal of this study was to realize pattern alterations for women’s suits by using the spatial distribution of distance ease in the body-garment interface. Design/methodology/approach An unclothed mannequin and the mannequin clothed with seven suits having different ease allowances were scanned by a 3D body scanner respectively. The image of the unclothed mannequin was then superimposed on that of each clothed mannequin (suit) to exhibit the differences in ease distribution among these suits. The distance eases at ten selected body landmarks were determined by measuring the gaps between the body and suit surfaces. Findings The mathematical models of ease distributions were built through the regression analysis to predict the distance ease with a given ease allowance. After the verification with the actual measurements, these ease distribution models could provide localized distance eases for alternating pattern pieces to ensure a specified ease allowance. Originality/value In order to realize the automatic generation of garment patterns, the ease distribution between a human body and a garment is crucial because ease is one of the determinants for garment fit. This study demonstrated a new approach of automatic pattern alteration based on 3D scanned data to accelerate the pattern making process for women’s suits with customized ease allowance.