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<p>This study introduces systematic fit adjustment frameworks to improve garment fit for diverse body types, and describes the development of a body type optimized pattern library for Made-to-Measure (MTM) apparel systems. The frameworks were designed to standardize adjustments by addressing structural and detailed fit imbalances. Unlike traditional methods reliant on empirical judgment, this approach identifies key fit imbalance factors-proportion, drop, and lateral posture- through expert evaluations of a block pattern on 126 virtual 3D fit models (size 100; chest circumference 98.5–101.4&#xa0;cm). Based on these evaluations, a total of 48 optimized patterns were generated. These are organized in a three-dimensional pattern matrix combining: three proportion types (P, R, T), four drop types (Y, A, B, BB), and four lateral posture types (NN, NS, SN, SS), thereby permitting precise morphological customization. Expert evaluations using 11 criteria demonstrated that optimized patterns significantly outperformed the block pattern (p &lt; 0.001) in ease allowance, balance, and seam alignment. Subsequent real-fit validation with muslin prototypes confirmed improvements in garment balance and reductions in drag lines, gaps, and hikes, verifying the effectiveness of virtual modifications. The introduced pattern library and frameworks offer a structured foundation for integrating modular pattern systems into digital MTM platforms, facilitating scalable automation and advancing mass customization with significantly improved fit.</p>

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Development of a body type optimized pattern library based on fit adjustment frameworks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月29日

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  • Jiyoung Choi,
  • Hee Eun Choi

摘要

This study introduces systematic fit adjustment frameworks to improve garment fit for diverse body types, and describes the development of a body type optimized pattern library for Made-to-Measure (MTM) apparel systems. The frameworks were designed to standardize adjustments by addressing structural and detailed fit imbalances. Unlike traditional methods reliant on empirical judgment, this approach identifies key fit imbalance factors-proportion, drop, and lateral posture- through expert evaluations of a block pattern on 126 virtual 3D fit models (size 100; chest circumference 98.5–101.4 cm). Based on these evaluations, a total of 48 optimized patterns were generated. These are organized in a three-dimensional pattern matrix combining: three proportion types (P, R, T), four drop types (Y, A, B, BB), and four lateral posture types (NN, NS, SN, SS), thereby permitting precise morphological customization. Expert evaluations using 11 criteria demonstrated that optimized patterns significantly outperformed the block pattern (p < 0.001) in ease allowance, balance, and seam alignment. Subsequent real-fit validation with muslin prototypes confirmed improvements in garment balance and reductions in drag lines, gaps, and hikes, verifying the effectiveness of virtual modifications. The introduced pattern library and frameworks offer a structured foundation for integrating modular pattern systems into digital MTM platforms, facilitating scalable automation and advancing mass customization with significantly improved fit.