Factors Affecting Apparel Pattern Grading Accuracy: Existing Software Solutions Comparison and Development of New Solution

Every so often, grading is not 100% accurate due to the conventional system for calculating the grading incre¬ment. The aim of this study was to develop a new calculation system of grading increment provided by different software, e.g. Lectra, Gerber, Optitex, Boke CAD etc., and to develop a new mathematical solution that enhances grading precision. For this experiment, three different spec sheets of different buyers were collected, and then combined and drawn to a solitary sketch for both front and back including all points of measures (POM) for a more easy comparison. The solutions for the presence of diagonal and curve measurements were provided with examples using various tools and techniques of different professional garment CAD software. The benefit of the new approach is not only reduced errors of grading but also guaranteed garment fit without distorting style features. However, the drawbacks of the measurement method are complicated and time-consuming. They revolve around the fact that iterative fitting and adjustments are mandatory to improve the fit before bulk production. The study revealed that this new system slightly increases calculation time, whereas the sample approval time for order execution reduces considerably.

A study of consumers’ pre-purchase evaluation of apparel products in Ethiopia

PurposeThe purpose of this paper is to explore pre-purchase apparel evaluation cues and examine the effect of demographic variables empirically in the context of a developing country. The initiation for the study was driven by the absence of such prior research and supplemented by the big market opportunity for clothing products in the country under investigation.Design/methodology/approachA self-administered online survey was used for data collection. Demographic questions, 23 apparel measurement items composed of 17 product-based and 6 sustainability-based items, and an open-ended question were included in the questionnaire. Factor analysis was used for dimension reduction and one-way multivariate analysis of variance (MANOVA) for hypotheses testing.FindingsGarment fit, design features, colour, fabric type and seasonality are the five important characteristics most Ethiopian consumers consider while buying apparel products. Factor analysis resulted in five important factors used for pre-purchase apparel evaluation amongst which the design and extrinsic cue is found to be the most important. Environmental factors in the apparel industry got higher emphasis than social factors. While age and educational background made differences in apparel evaluation, gender did not show a significant difference.Originality/valueThe paper provides a founding insight in exploring apparel evaluation cues by considering product- and sustainability-based cues in a developing country context. It also examines the effect of three demographic variables which are rarely studied in such combination.

Garment Fit Evaluation for Fashion Design and Manufacturing

Currently, garment fit evaluation is one of the biggest bottlenecks for fashion design and manufacturing. In this paper, we proposed a garment fit prediction model using data learning technology, such as Back Propagation Artificial Neural Networks (BP-ANNs), Random Forest, Bayesian Classifier. The inputs of the proposed model are digital clothing pressures measured by virtual try-on; while the output of the model is one of the three fit conditions- tight, fit or loose. In order to acquire reliable learning data, virtual and real try-on experiments were carried out to collect input and output learning data respectively. After learning from the collected input and output experimental data, the proposed model can predict garment fit rapidly and automatically by inputting digital clothing pressures measured by virtual try-on. Test results showed that the prediction accuracies of data-learning-based garment fit evaluation methods are much better than that of traditional methods.

Consumer expectations and perception of clothing comfort in sports and exercise garments

Purpose Sports garments play an important role in the well-being of an athlete by protecting the wearer from changing environmental conditions and providing a comfortable feel. Clothing requirements have changed in recent years and demand for apparel with a higher comfort performance has been rising. Hence, the purpose of this study is to explore consumers’ expectations and perception of comfort and to examine how different textiles are perceived by consumers to provide useful knowledge that allows to engineer comfort into fabrics and sports garments. Design/methodology/approach This online survey comprised 292 respondents, classified by sex, age, nationality and physical activity. The respondents were asked a total of 18 questions through the Bristol Online Survey tool to explore expectation, perception and preference of clothing comfort, specifically of sportswear. Findings Fit and comfort are closely linked together, both forming part of the clothing comfort concept. When purchasing garments online, the haptics of fabrics were identified as a crucial missing parameter. However, priorities of attributes within the concept varied according to the person’s sex and nationality. Women put more emphasis on garment fit and showed a higher need for tactile input, whereas men prioritised physiological comfort descriptors, i.e. properties which facilitate thermoregulation. Furthermore, there is an increased importance of physiological comfort parameters for people exercising for 10 or more hours per week. Finally, it was possible to identify common associations and preferences for textile materials (cotton, polyester, cotton/polyester blend and wool). However, consideration should be taken concerning sex and nationality. Originality/value Sex and nationality are parameters modulating the clothing comfort concept and the conceptualised feel of materials. Therefore, the sex and nationality of the end-consumer should be considered during the development phase of sports garments and particular attention should be given to the targeted market in which these will be sold.

DEVELOPMENT OF CLIMBING CLOTHES USING COMPUTERIZED 3D CLOTHING SIMULATION

The purpose. This paper aims to explore the applicability of computerized 3D virtual clothing simulation programs for the development of patterns for men’s rock climbing pants. Methodology. The experiment involved comparative methods of virtual wearing, as well as pressure points, perspective map and appearance evaluations. The theoretical and methodological basis of the research are scientific works presenting data from 3D virtual fitting software. Results. The study selected three designs of climbing pants to compare the layout of patterns and to analyze the appearance through virtual garment fit maps. First, the 2D-to-3D was applied to develop garment patterns of a rock climber’s pants. The patterns were developed directly in the 3D software. The flat patterns can be visualized in the sewing/assembly stage of the prototype. Then, 3D animation technology was used in simulation. The results throughout the study were: 1) 3D simulation of three different types of pants design. 2) Evaluating the fit and comfort of the clothing using garment fitting maps. 3) Identifying the most discomforting parts of the patterns, thigh, hip, waist, pants hem and knee girth. 4) Modify and adjust the clothing according to the evaluation situation. Scientific novelty consists in providing a systematic methodology of utilizing 3D simulation for a specific category of wearers, such as rock climbers. Practical significance. The considered research directions of 3D clothing simulation can be used by designers and patternmakers in the creative and experimental process of designing clothes expanding the possibilities of software to transform functional design in the industry and research.

Neural Correlates of Garment Fit and Purchase Intention in the Consumer Decision-Making Process and the Influence of Product Presentation

In today’s competitive e-commerce markets, it is crucial to promote product satisfaction and to quickly identify purchase intention in decision-making consumers. The present investigation examined the relationship between perceived garment fit and purchase intention, together with how product presentation methods (mannequin versus self-model) contribute to decision-making processes of clothing. Thirty-nine female volunteers were scanned using fMRI while performing an online shopping task. In Part 1, univariate analysis was conducted between garment fit and product presentation factors to assess their effects on purchase deliberation. In Part 2, univariate, multivariate pattern, and psychophysiological interaction analyses were carried out to examine the predictive ability of fit evaluation and product presentation on purchase intention. First, garment fit × product presentation interaction effects on purchase deliberation were observed in the frontopolar cortex, superior frontal gyrus, anterior cingulate cortex, and posterior cingulate cortex. Part 2 demonstrated neural signals of the dorsomedial prefrontal cortex, premotor cortex, supplementary motor area, superior parietal lobule, supramarginal gyrus, superior temporal sulcus, fusiform gyrus, and insula to distinguish subsequent purchase intentions. Overall, the findings denote directed exploration, visual and action processing as key neural processes in decision-making that uniquely reflect garment fit and product presentation type during purchase deliberation. Additionally, with respect to the effects of purchase intention on product evaluation, the evidence conveys that mental interactions with products and social cognition are fundamental processes that capture subsequent purchase intention at the product evaluation stage.

Body-mapping based on thermographic measurements under different influencing factors

The presented research focuses on the use of thermography to observe influences of different influencing factors on the changes of lower body temperatures. Based on the outcomes of the research fit of garment plays an important role for the retention of heat between the textile material and skin, especially in the zone of both upper and lower shin. Also only tight model of trousers is capable to preserve the temperature in zones of thigh and knees of male volunteer. The given body-maps present differences in values of average temperatures (ΔT) considering the garment fit. In the indoor environment, tight model of female trousers has significant influence on the average temperature in the zone of upper shin. This observation is even more pronounced in the outdoor environment. For the male volunteer, higher differences, between temperatures are seen in the outdoor environment only.

Body Size Measurement Using a Smartphone

Measuring body sizes accurately and rapidly for optimal garment fit detection has been a challenge for fashion retailers. Especially for apparel e-commerce, there is an increasing need for digital and convenient ways to obtain body measurements to provide their customers with correct-fitting products. However, the currently available methods depend on cumbersome and complex 3D reconstruction-based approaches. In this paper, we propose a novel smartphone-based body size measurement method that does not require any additional objects of a known size as a reference when acquiring a subject’s body image using a smartphone. The novelty of our proposed method is that it acquires measurement positions using body proportions and machine learning techniques, and it performs 3D reconstruction of the body using measurements obtained from two silhouette images. We applied our proposed method to measure body sizes (i.e., waist, lower hip, and thigh circumferences) of males and females for selecting well-fitted pants. The experimental results show that our proposed method gives an accuracy of 95.59% on average when estimating the size of the waist, lower hip, and thigh circumferences. Our proposed method is expected to solve issues with digital body measurements and provide a convenient garment fit detection solution for online shopping.

Analysis of segmented elderly women’s lower bodies using 3D-LOOK scan data and virtual representation

To address the clothing needs of an aging society, this study developed a scale using three-dimensional (3D) scan data that determine elderly women’s lower body shapes to improve the way garments fit the elderly. Body type elements that play an important role in garment fit were identified and five body type elements were selected for use in this study. A stepwise discriminant analysis using 176 dimensions was performed to extract parameters reflecting body shape features, resulting in 37 parameters. A scale for determining body shapes was developed using the discriminant function equation. This study differs from existing studies on body shape classification in that we determined the diverse body shape features of individuals by extracting the lower body type elements related to garment fit. This study demonstrated an organic relationship among lower body types, where a greater posterior pelvic tilt was associated with a protruding lower abdomen, flat buttocks, and an o-type frontal leg shape. The significance of this study lies in the extraction of 3D parameters that reflect the body shape features of elderly women. Such 3D parameter data can be used to create personal virtual bodies in online shopping malls in the future.