(untitled) (Journal of Fiber Bioengineering and Informatics, 2024)
With the growing demand for personalized clothing, the children’s apparel market is receiving significant attention. Traditional pattern-making methods often fail to accommodate the diverse body shapes and preferences of preschoolers, negatively impacting consumer satisfaction. Although existing studies have examined various pattern-making techniques, they frequently overlook the unique needs of children, resulting in limited customization options and reduced efficiency. Therefore, a comprehensive approach is necessary to effectively integrate body size data with personalized pattern-making rules. This study investigates a parameterized model for generating personalized children’s clothing paper patterns. It aims to streamline the production process while catering to personalised preferences. Through the analysis of preschooler body size data, 24 body type features are identified, leading to the development of a discrimination model based on principal component analysis and support vector machine. This model, integrated with clothing pattern-making rules, enhances the structure of paper patterns. Furthermore, a parameterised paper pattern for children’s clothing is created, utilizing children’s body data to generate tailored paper patterns efficiently. Additionally, a linkage model combining 3D and 2D aspects is employed to evaluate clothing fit and overall effects through virtual try-on simulations. Findings suggest reduced production complexity, time, and improved efficiency and quality in personalized pattern making.