Purpose In the present study, two subjective approaches including Thurston's method of paired comparisons and the rank order method were employed to evaluate seam puckering. Design/methodology/approach To this end, test samples were produced using five different sewing thread tension levels and three stitch lengths. The degree of seam puckering was assessed according to the AATCC 88B subjective evaluation standard, as well as through Thurston's paired comparison and rank order methods. Findings Findings revealed that all three subjective methods confirmed the increase in seam puckering severity with higher thread tension and longer stitch length. Furthermore, the correlation coefficients between the AATCC 88B standard and the two applied methods exceeded 0.96, indicating strong consistency. These results demonstrate that Thurston's paired comparison and the rank order methods can be reliably employed for seam puckering evaluation. Originality/value Although various objective methods have been proposed in previous research to assess seam puckering, subjective approaches such as Thurston's paired-comparison method and rank-order evaluation have not been employed for this purpose before.
Purpose Research on simulating shaped shoe uppers is still in its early stages. Most studies focus on simpler cut uppers, using texture mapping principles to directly map loop patterns onto curved surfaces. Although this method simplifies the simulation process to some extent, it fails to fully capture the complex details and realistic textures of the shoe uppers. For applications requiring high-precision simulations, the current technology clearly has limitations. Therefore, developing more refined and accurate methods for simulating shaped shoe uppers becomes particularly important. Design/methodology/approach This study focuses on flat-knitted shaped shoe uppers, delving into the knitting processes and the selection of different organizational structures for various regions of the shoe upper. It also comprehensively examines the basic principles of optimizing and reconstructing DXF files, as well as the three-dimensional (3D) simulation workflow. Findings The study addresses the issue of 3D surface control points exceeding OBJ boundaries, proposing a rational and efficient surface triangulation indexing method that successfully resolves this challenge. Originality/value This study helps in enabling the rapid transformation of planar loop meshes and shape value points onto curved surfaces. It also allows for the precise acquisition of the spatial coordinates of the shape value points for each loop in the fully-fashioned shoe upper and also helps in achieving the digital design and 3D simulation of flat-knitted shaped uppers through the implementation of loop structures.
Purpose The purpose of this study is to address workflow fragmentation in conventional fabric weave design, where weave drafting, fabric preview, file export and loom-side data transfer are often handled through separate tools or manual operations. This paper presents an integrated, lightweight, web-based platform for interactive woven fabric design, Canvas-based fabric visualization and LAN-based loom data distribution, aiming to improve cross-platform accessibility, design-data continuity and sampling preparation efficiency in digital weaving environments. Design/methodology/approach The platform was implemented using a browser-based front end based on the HTML5 Canvas API and native JavaScript, together with a lightweight Node.js/Express data distribution service for local-area-network deployment. The system supports professional weave-symbol drawing, left/right mouse-button configuration, grid-based linkage among the fabric weave diagram, drafting plan and lifting plan, adjustable yarn arrangement, material-related rendering parameters and DY-format export. Instead of full three-dimensional physically based rendering, the visualization module adopts a lightweight Canvas-based rendering strategy that combines weave-matrix interpretation, yarn colour arrangement, procedural texture and simplified light-shadow effects. Experimental evaluation included drawing response-time measurement, expert-based visual consistency assessment of simulated fabrics against real samples and stability testing of LAN-based file distribution for the SGA598 full-automatic rapier sampling loom. Findings The drawing module achieved average response times below 30 ms for weave diagrams up to 500 × 500 cells, indicating that the platform can support real-time interactive editing under the tested conditions. In the expert visual consistency evaluation, the chemical-fibre fabric simulation achieved an overall mean score of 4.22/5, while the cotton fabric simulation achieved an overall mean score of 3.96/5. The results suggest acceptable visual consistency, although light-shadow expression received lower scores than texture reproduction, indicating that lighting simulation remains a limitation of the current Canvas-based model. The LAN-based data distribution test achieved 100% successful upload and download/import operations in the controlled SGA598 sampling-loom environment, with an average latency of less than 250 ms, demonstrating baseline stability under the tested local-network scenario and reducing reliance on removable storage devices in this context. Originality/value This study presents a lightweight browser-based implementation that integrates fabric weave drafting, grid-based drafting/lifting-plan linkage, Canvas-based fabric visualization, DY-format export and LAN-based loom-file distribution into a unified workflow. Its originality lies not in replacing existing textile CAD/CAM or simulation systems, but in connecting the practical stages of weave editing, fabric preview, loom-readable file generation and local data distribution for sampling-loom preparation. The platform provides a workflow-oriented approach for improving design-to-loom data continuity in the tested SGA598 sampling-loom scenario, while future extension to other loom systems will require additional file-format adapters and communication interfaces.
Purpose The textile and apparel industry is a significant contributor to solid waste generation, and the transformation of these wastes into value-added products is essential for advancing circular economy principles and achieving zero-waste objectives. In this study, a waste-to-functionalization approach was employed to upcycle textile waste into activated carbon (AC), which was subsequently applied onto textile substrates to develop odor-control functional fabrics. Design/methodology/approach Activated carbons were produced from three types of textile waste materials: cotton (CO), cotton and/or polyester (PES) blends and hemp (HA)-based fabrics. These carbons were applied onto cotton and polyester fabrics using a conventional screen-printing technique. For comparative evaluation, a commercial AC product was also utilized. The odor control performance of the treated fabrics was assessed using gas chromatography–mass spectrometry. Air permeability, water vapor and thermal resistance and moisture management characteristics were also tested. Findings Results demonstrated that activated carbons derived from textile waste significantly reduced odor emissions compared to both untreated fabrics and those treated with commercial AC, with the most pronounced effect observed in cotton fabrics. Furthermore, fabrics printed with textile waste-derived activated carbons exhibited acceptable water vapor resistance and improved moisture management characteristics. Originality/value This study thus presents a new and viable waste management strategy for the valorization of textile waste, while simultaneously imparting odor-control functionality to fabrics, indicating potential for applications in sportswear and casual wear.
Purpose This study aims to research the effects and mechanisms of fluoresce-quercitrin in green silk on skin cancer melanoma inhibition, so as to provide a reference for the medical applications of natural green silk. Design/methodology/approach The research focuses on the biological functions and mechanisms of fluoresce-quercitrin in green silk. Therefore, green cocoons were used for quercetin extraction and purification, following traditional ethanol-water extraction and chromatographic purification methods. In the cell experiment, normal skin keratinocytes HaCaT and malignant melanoma cells A375 were selected. Fluoresce-quercitrin extracted from green silk was used for cell culture, followed by detection of cell phenotype, including cell activity and tumor malignancy. At the same time, transcriptome analysis and pathway enrichment analysis were performed on experimental cells to explore the mechanism by which fluoresce-quercitrin affects cell activity and anticancer activity. Findings Fluoresce-quercitrin have no significant toxic impact on normal skin cells but has anticancer effects, including inhibiting 30% of melanoma proliferation and promoting 8% of cell apoptosis as well as arresting cell cycle. In addition, fluoresce-quercitrin also inhibit 75% of tumorigenic and 89% of metastatic abilities of melanoma cells. Except for the oxidation and phenotype-related cycle pathways related to quercetin, fluoresce-quercitrin is found to be involved in DNA repair, including pathways such as P53 and mismatch repair. Meanwhile, the effects mechanism of fluoresce-quercitrin in tumor cells and normal cells involves not only specific pathways, but also partially overlapping and opposite changes. Originality/value The experimental results indicate that the fluoresce-quercitrin contained in natural green silk has a protective effect on normal cells, along with a killing effect on tumor cells. Transcriptome comparison and pathway enrichment analysis further support and explain the bidirectional influence effect on normal cells and tumor cells.
Purpose This study aims to predict the effects of different bra component materials on breast deformation during running using a finite element (FE) method. Breast deformation represents a significant concern for women during physical exercise, as it can lead to discomfort and potential injury. The use of sports bras effectively reduces breast displacement and provides protection. In most experimental studies, reproducibility is frequently compromised by external environmental conditions and individual subject differences. The FE analysis method offers substantial advantages by significantly shortening research duration, reducing experimental costs and ensuring high reproducibility. Design/methodology/approach In this study, a three-dimensional (3D) scanning technique was employed to acquire a breast model from a manikin. Reverse engineering and computer-aided design software were utilized to construct assembly models for FE analysis. Pre-processing, solving and post-processing were conducted within FE simulation software to obtain breast deformation outcomes. Shoulder straps, cups and underbands fabricated from different materials were simulated to evaluate the influence of component materials on breast deformation. Findings The root mean square error of the calculations was less than 1, indicating that the FE calculation results for all models exhibited good agreement with experimental data. Different component materials demonstrated distinct effects on breast deformation. Specifically, the cup material with the highest elastic modulus, the shoulder strap material with the highest elastic modulus and the underband composed of polyester material significantly reduced breast displacement. Originality/value This study establishes a methodological framework for predicting deformation at various positions on the breast surface, thereby facilitating the effective design of sports bras.
Purpose Sustainable development is a pressing topic in contemporary society, posing significant challenges to global healthcare systems, particularly in regions with limited medical resources. This study aims to fabricate an eco-friendly fabric-like pH indicator for healthcare via sweat to contribute to sustainability.Design/methodology/approach Fabric-like bacterial cellulose (BC)/anthocyanin derived from Clitoria ternatea was fabricated using a textile padding method. The characteristics of fabricated material were investigated via colorimetric parameters and fastness, field emission scanning electron microscopy, material pH and colorimetric sensation under sweat pH change.Findings The resulting composite exhibits notable properties such as high color strength (K/S) of 15.59 +/- 0.73 at max = 580 nm, surpassing conventional textile materials like cotton, lyocell, silk and wool (2.4-13 times higher). A comprehensive investigation into the color variation of BC/anthocyanin in response to different pH levels of artificial sweat was provided, facilitating health monitoring, particularly for individuals with communication challenges.Originality/value This study establishes an efficient padding process for the fabrication of fabric-like BC/anthocyanin composites, designed to serve as pH indicators in medical textiles. The approach offers added value to the textile industry by providing a sustainable alternative to conventional materials and laying the groundwork for further research on BC dyeing and functionalization. By utilizing industry-available technologies such as padding, this work supports the scalable development of functionalized BC.
Purpose This study addresses the critical environmental challenge posed by the incineration of waste textiles, a process known to emit significant quantities of greenhouse gases and atmospheric pollutants. The research aims to provide valuable insights for optimizing waste textile combustion conditions, with the potential to reduce the environmental impact of incineration by mitigating greenhouse gas emissions and atmospheric pollutant production.Design/methodology/approach Employing a sophisticated cone calorimeter, the co-incineration behavior of polyester-cotton mixed fabrics (PCMFs) across varying ratios was analyzed. Novel indicators were introduced to scrutinize the smoke emission and carbon release characteristics of the PCMFs during co-incineration.Findings The results indicated that fabrics with higher polyester content ignited faster and achieved higher peak heat release rates. Notably, the PCMFs with lower polyester levels displayed multiple combustion peaks due to the interaction between the polyester's lower melting point and the cotton fibers "scaffold effect". The total heat release (THR) of PCMFs initially increased and then decreased with rising polyester content, peaking at approximately 65% polyester. The THR also escalated with higher thermal irradiance, while the differences among various PCMFs were minimal. A higher polyester content resulted in a more substantial and rapid carbon release, and increased thermal irradiance intensified the carbon release.Originality/value A co-incineration method of PCMFs with a 10% gradient was proposed. New indicators were introduced to analyze pollutant emission of PCMFs. Carbon release intensity was affected by thermal irradiation, combustion time and ratio. Some advice was given to reduce environmental pollution caused by waste textile incineration.
PurposeIn textiles, dual-core yarn emerges as a remarkable innovation, made of two different core materials. This study aims to examine the characteristics of dual-core yarn manufactured from different sheath components, such as cotton (CO)/micro acrylic (MAC) blend (40/60% CO/MAC), CO/micro modal (CMD) blend (40/60% CO/CMD) and 100% CO fiber.Design/methodology/approachThe core materials for all yarns were elastane and T400 (R) filament, incorporated using a modified ring frame. The influence of sheath components on yarn characteristics was evaluated. Statistical analysis using one-way ANOVA was conducted to determine significant differences in strength, elongation, unevenness, hairiness and yarn quality index among the produced yarns from the three sheath materials. Additionally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank the yarn samples based on overall performance.FindingsThe use of different sheath components significantly influenced the yarn characteristics. Yarn produced with the 40/60% CO/CMD blend emerged as the most suitable, while yarn produced from 100% CO ranked as the least favorable. The ranking of yarn samples was performed using the TOPSIS multi-criteria decision-making method based on key yarn performance parameters. These findings reflect the critical impact of sheath fiber selection and yarn count on performance outcomes.Originality/valueThis study presents a novel investigation of dual-core yarns incorporating CMD and MAC fibers as sheath blends with CO while using elastane and T400 (R) filaments as dual-core components. Unlike previous studies that mainly used conventional CO or CO-polyester sheath materials, this work systematically explores the influence of micro fiber-based sheath blends on yarn performance. The proposed approach provides a structured method for optimizing dual-core yarn design for advanced textile applications.
Purpose People are often drawn to models' attractive clothing, which sparks their desire to search for and purchase such items. However, consumer preferences vary - some favor full outfits, while others focus only on lower garments. Tiled clothing images greatly boost search success rates. Thus, we propose an improved attention-based conditional GAN to generate such tiled images, which can produce clear, accurate, category-compliant results from models' full-body images.Design/methodology/approach We propose a two-stage framework, where the first stage generates coarse images and the second stage generates fine images: Stage 1 employs CBAM attention to enhance skip connections in U-Net, suppressing irrelevant information like human bodies while sharing low-level features. Labels are concatenated at the bottleneck layer to specify the generated clothing category. We modified the original residual blocks by integrating CBAM attention into them and applying this to Stage 2 of our model, mitigating gradient vanishing while further suppressing irrelevant information. The improved network, termed AttCloth-GAN, can generate clothing images of specified categories.Findings Experimental results demonstrate that our method achieves optimal performance while generating images that align with category labels. On the dresscode dataset, our model improves upon pix2pixHD by 3.0% in SSIM and reduces FID by 10.0%.Originality/value Our proposed method can convert full-body model images into tiled clothing images of specified categories, meeting diverse user needs. Users can extract clothing from their preferred body parts for retrieval, significantly enhancing search accuracy and effectively promoting the development of online clothing shopping.
Purpose - To more accurately predict user preferences in the clothing industry, a user clothing preference prediction model combining the KCCF model and BP neural network model was proposed. Design/methodology/approach - Using men's suits as an example, the model first constructed a Kansei space using semantic differential and factor analysis methods, establishing 8 pairs of representative Kansei words. The shape, color, and texture dimensions of 100 men's suits were then quantified. Next, a coupling coordination model was used to establish the coordination relationship between these dimensions and complete their quantification. Finally, a 7 & times; 15 & times; 1 BP neural network model was constructed to predict user preferences and measure the model's accuracy. Findings - The results were compared with traditional one-dimensional and the proposed three-dimensional models. The 7-dimensional model performed exceptionally well on all prediction accuracy metrics, significantly improving prediction accuracy. Originality/value - The combined KCCF and BP neural network prediction model demonstrates strong operability, not only effectively supporting designers in predicting user preferences, but also providing guidance for scheme selection, design optimization, and market decision-making in clothing product development.
Purpose - This study aims to investigate the thermal and moisture transport properties of cross-tuck knitted fabrics designed for hot and humid climates. The research evaluates how different yarn compositions (100% cotton, 50/50 and 70/30 cotton-polyester blends) and knitting structures (plain, single, double and triple cross-tuck stitches) influence fabric performance. By analyzing air permeability, moisture management, thermal conductivity and bursting strength, the study seeks to establish design guidelines for optimizing knitted textiles in extreme weather conditions. The findings will contribute to advancing functional fabric engineering for improved thermal comfort in high-temperature environments. Design/methodology/approach - Three yarn compositions (100% cotton, 50/50 and 70/30 cotton-polyester blends) were knitted into four structures (plain, single, double and triple cross-tuck stitches) using a circular knitting machine. Fabric samples were developed and tested for air permeability (ISO 9237), moisture management (AATCC 195), thermal conductivity (KAWABATA KES-FB-7A) and bursting strength (ISO 13938-2). Statistical analysis (ANOVA and Tukey test) evaluated the effects of yarn composition and knitting structure on fabric properties. The experimental approach combined material characterization with standardized testing to assess performance under controlled conditions. Findings - The 70/30 polyester-cotton blend exhibited superior mechanical properties (tenacity: 25.16 CN/Tex) compared to 100% cotton. Air permeability peaked in double cross-tuck structures (1,323 mm/s for 70/30 blend), while single cross-tuck showed optimal moisture management (OMMC: 0.60). Thermal conductivity remained consistent across structures (0.00005-0.00007 W/cm degrees C), with yarn composition exerting greater influence than stitch type. Bursting strength was highest in 70/30 single cross-tuck (307.8 kPa). Fabric thickness increased linearly with tuck-stitch frequency. Statistical analysis confirmed that yarn composition significantly impacted all properties (p < 0.05), while knitting structure primarily affected air permeability and moisture management. Originality/value - While most of the previous research investigates the properties of standard knitting patterns, cross-tuck stitches remain largely unexplored. This study fills that gap by analyzing how these specific stitches manage heat and moisture in hot and humid conditions. By testing plain knits against single, double and triple cross-tuck structures in both 100% cotton and cotton-polyester blends, we reveal how stitch complexity impacts the heat and moisture transfer properties of a textile fabric. The outcomes of the study help to develop more effective textile solutions with enhanced heat and mass transfer properties for challenging climatic conditions.
PurposeNowadays, the labeling of clothing images on e-commerce platforms mainly relies on manual labor, which not only wastes manpower and time, but also causes problems such as inconsistent labeling standards among merchants and cognitive differences among different annotators. Besides, it is particularly difficult to make image annotation for dresses with rich style variations and massive image data. To solve this problem, in this study, an automatic recognition method of multi-label images based on deep learning is proposed.Design/methodology/approachIn this paper, first dress images were selected whose attributes were annotated and a dataset for the dressing images was established according to the style of silhouette, collar, and sleeve. Then based on the baseline networks, a multi-label dress image recognition method, the VE-DE network, was proposed, which combines VGG-19, DenseNet-201 and the channel attention network.FindingsThe results show that the average precision mean (mAP), precision, and recall of the dress images in the VE-DE recognition test set were 93.9%, 91.6%, and 90.3%, respectively, all higher than the three baseline networks. Among them, mAP was 4.3%, 11.1%, and 3.7% higher than VGG-19, ResNet-101, and DenseNet-201, respectively. VE-DE network can improve the vulnerability of the baseline network to the influence of external factors when identifying the profile and has a good recognition effect on the samples with easily confused profiles. Visual analysis of the class activation heatmap and intermediate layer activation of the VE-DE model shows that it can focus on such parts of the dress as the collar, sleeves, and waist, which contribute significantly to the recognition of the images, with the waist being the most highly focused area.Originality/valueThe automatic recognition method of multi-label images can be used in an e-commerce platform. With the help of this technology, e-commerce platforms can automatically and quickly label clothing images, reduce labor costs, improve product labeling speed and quality.
PurposeThis study establishes a computer modelling workflow to develop curvature-driven human-mimetic surfaces for enhanced fabric drape testing. Standard drape testing relies on flat disc-shaped supporting tables. These tables fail to replicate mechanical textile-body interactions governed by complex geometries of anatomical surfaces. The new approach aims to enhance deformation analysis by creating shapes representing curvature of the body while ensuring compatibility with existing protocols.Design/methodology/approachThe four-stage workflow analysed curvature metrics (mean, Gaussian and principal) across 10 ASTM-compliant female avatars (sizes 2-20; 301.941 sampling points), classified surfaces into convex, saddle and concave types and synthesised parametric models using four curve forms, namely cubic B & eacute;zier, cubic B-spline, quadratic B & eacute;zier and quarter-ellipse. The generated surfaces were validated via seven non-parametric statistical indicators and evaluated in Clo3D simulations by comparing stress distributions and drape properties against flat discs.FindingsCurvature analysis revealed that convex (45.4-49.7%) and saddle (48.6-52.7%) surfaces dominate anatomical topography. Quarter-ellipse models achieved superior curvature replication, up to 99.6% coverage. Simulations confirmed human-mimetic surfaces localise warp/weft stresses on protrusions and shear stresses in transitions, aligning with stress distribution in clothing, unlike uniform stress distribution on flat discs. An open-access library of 34 validated surfaces (17 convex-saddle pairs, diameters 20-857 mm) was published.Originality/valueThis is the first approach to reproduce curvature of the human body in supporting tables for drape testing, enabling anatomically driven fabric-body interaction modelling. The parametric workflow, curvature database and published library of 3D models bridge anthropometric diversity with textile performance analysis for both digital and physical testing.
PurposeThe purpose of this paper is to propose an automatic generation method integrating parametric design and artificial neural networks for women's jackets patterns to improve the efficiency of personalized garment pattern-making.Design/methodology/approachFirstly, the structure of the jackets is divided based on the modular design concept, and a library of modular component patterns is established. Subsequently, parametric modeling is employed to analyze the parameter constraints of the patterns. For the front and back panels of the jackets, three machine learning regression prediction methods - Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN) and Support Vector Regression (SVR) - are utilized to establish predictive models for key point coordinates. A comparative analysis of the prediction results from these three models is conducted.FindingsThe comparative analysis reveals that BPNN achieves the highest accuracy in predicting key point coordinates, enabling more precise fitting of the coordinate values of critical points in the pattern. By integrating the parametric design approach with the predictive model, an automatic pattern generation system based on the AutoCAD platform is developed. To validate the feasibility of the proposed method, virtual try-on experiments are conducted using 3D virtual fitting technology, and the garment's performance is evaluated. The results confirm the rationality and effectiveness of the proposed automatic pattern generation approach. However, this study has several limitations: the small sample size (n = 25) leads to overfitting (R = 1.0000 on the test set is unrealistic); virtual fit was evaluated only qualitatively and no comparison with human pattern makers was performed. Therefore, the results should be considered preliminary. Future work with larger datasets and quantitative fit metrics is required.Originality/valueThis research provides a reference for the rapid generation of garment patterns and the development of personalized garment customization.
PurposeThis paper aims to analyze the contact pressure and displacement in varying support strength bras at different movement speeds using finite element (FE) simulation, providing a theoretical and data-driven basis for designing comfortable and functional sports bras.Design/methodology/approachUtilizing a 3D scanning system, reverse engineering software, and SolidWorks, a "torso-breast" model and a bra geometric model were constructed. ABAQUS was then used to develop a "torso-breast-bra" FE contact mechanics model. Material properties and constraints were adjusted to simulate dynamic and static pressure distributions and breast displacements under varying support strengths and movement velocities.FindingsThe root mean square errors (RMSE) between the simulated and actual results for contact pressure and displacement were 8.13% and 10.11%, respectively, with a high correlation coefficient (R) of 0.97, indicating accurate simulations. As the intensity of the exercise escalates, fluctuations in pressure within the breast area become more accentuated, particularly in the lower breast region. Concurrently, the range of relative displacement of the breasts in the X, Y and Z directions also escalates. Specifically, when the speed increases from 6 km/h to 8 km/h, the displacement increment in the Z direction is approximately twice that in the X direction. In addition, wearing a high-support sports bra effectively reduce breast motion and improve exercise comfort.Originality/valueThis research provides an effective method for predicting dynamic and static contact pressures and breast displacement, essential for improving bra design, enhancing comfort and protecting health.
PurposeThis study aims to improve the physical and mental well-being of postoperative female breast cancer patients by developing an innovative apparel design that addresses the lack of adequate rehabilitation products and guidance after discharge from medical institutions.Design/methodology/approachThe research integrates quality function deployment (QFD) and the theory of inventive problem solving (TRIZ) to guide the design process. Patient needs were collected and prioritized through QFD, which transformed them into actionable design requirements. TRIZ was then applied to resolve contradictions among the requirements and generate innovative design solutions. Based on this process, a postoperative apparel design model and prototype-based validation scheme were proposed and validated through scenario simulation and fuzzy evaluation model.FindingsThe resulting apparel design effectively addresses both physical rehabilitation and psychological recovery needs of female breast cancer patients after surgery. It also facilitates a more supportive and communicative environment between patients and medical staff, contributing to overall well-being.Originality/valueThis study provides a novel methodological framework for apparel design in the healthcare context by combining QFD and TRIZ. It offers a user-centered, problem-solving approach that enhances patient care through functional and emotionally supportive design.
PurposeThis study aims to address the limitations of treating human skin as a rigid body in existing body-clothing interaction models. A deformable virtual skin model has been introduced to more realistically simulate the complex, bidirectional mechanical interactions between the body and clothing, thereby enhancing the accuracy of apparel comfort assessment and virtual try-ons.Design/methodology/approachA virtual skin model composed of minute tetrahedral elements is constructed, with volume and shape constraints applied to simulate the biomechanical properties of the skin. By introducing an adjustable retention factor, the model can reflect the varying tightness of skin across different body regions. A mass-spring system was used for the fabric model, and a spatial hashing algorithm coupled with a momentum redistribution mechanism was implemented to efficiently and realistically handle the contact, collision and pressure transmission between the skin and fabric. Finally, the model's validity was verified by comparing the simulated pressure with real-world sensor data.FindingsThe experimental results demonstrate that, compared to the traditional rigid skin model (with an error of 33.20%), the flexible skin model using a uniform retention factor significantly reduces the error between simulated and real pressure to 17.72%. Furthermore, by assigning different retention factors to various body parts (e.g. chest, shoulders) to simulate regional skin property differences, the error was further reduced to 8.92%. This confirms that region-specific skin parameters more accurately reflect the actual body-clothing interaction.Originality/valueThis study presents a pioneering contribution by introducing and validating a dynamically deformable skin model for clothing simulation, supplanting the conventional rigid-body paradigm. By incorporating a retention factor and a bidirectional interaction mechanism, this model more precisely captures the complex deformation of skin under pressure, significantly improving the accuracy of clothing pressure prediction, thereby providing a more solid theoretical and technical foundation for the application of virtual simulation in apparel engineering, wearable technology and medical textiles.
PurposeThis study aims to achieve an accurate body type classification of professional athletes.Design/methodology/approachIn order to achieve body type classification of professional athletes, this article proposes a body type recognition method using a combination of joint spectral clustering, convolutional neural network and Taguchi test.FindingsThe results showed that the athletes' body types could be classified into four categories: small and compact (25.13%), tall and fit (26.67%), evenly proportioned and fit (24.62%) and limber (23.59%); the accuracy of the optimized convolutional neural network model was 99.43% and 97.14% in the training and test sets, respectively, and the loss rate was 2.49% and 5.12%, respectively.Originality/valueThe study is useful in facilitating research on the segmentation of professional athletes' body types and has practical value for the development of sportswear equipment. It also has some significance to the current research on body type classification and image recognition.
PurposeThis study aims to address the limitation in scheduling efficiency caused by subjective skill evaluations in apparel production lines by proposing a multi-objective worker scheduling optimization method based on the Analytic Hierarchy Process (AHP) and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). Design/methodology/approachAn evaluation system incorporating multidimensional skill indicators is first established using the AHP to determine factor weights, and workers' skill maturity is quantitatively assessed through normalization based on actual production data. A greedy initialization strategy and an adaptive mutation mechanism are then integrated into the NSGA-II framework to enhance convergence performance and solution space exploration, thereby improving its efficiency under practical scheduling constraints. Finally, optimization is conducted with skill maturity and production fluctuation minimization as objectives, yielding a set of near-optimal scheduling solutions. FindingsIntegrating AHP-based skill evaluation with an improved NSGA-II algorithm significantly enhances apparel production line worker scheduling, with the resulting solutions from case studies increasing workforce utilization by 11.5–15.3% and production output by 12.9–25.4%, while achieving faster convergence and higher solution quality. Originality/valueThe primary originality of this study lies in integrating AHP-based skill evaluation with production data, which is harnessed by an improved NSGA-II algorithm to efficiently tackle dynamic workforce allocation challenges, thereby significantly improving workforce allocation, computational efficiency, and solution quality.
