When designing a product that needs to fit the human shape, designers often use a small set of 3D models, called design models, either in physical or digital form, as representative shapes to cover the shape variabilities of the population for which the products are designed. Until recently, the process of creating these models has been an art involving manual interaction and empirical guesswork. The availability of the 3D anthropometric databases provides an opportunity to create design models optimally. In this paper, we propose a novel way to use 3D anthropometric databases to generate design models that represent a given population for design applications such as the sizing of garments and gear. We generate the representative shapes by solving a covering problem in a parameter space. Well-known techniques in computational geometry are used to solve this problem. We demonstrate the method using examples in designing glasses and helmets.
A novel approach for the construction of apparel sizing systems is formulated. As a first step to this process, efficient sizing systems are defined based on a mathematical model of garment fit. Nonlinear optimisation techniques are then used to derive a set of possible sizing systems using multidimensional information from anthropometric data. The method is illustrated by developing a sizing system designed for a dress shirt of a military uniform using anthropometric data from the US Army. Results of this analysis show that endogenous size assignment and selection of disaccommodated individuals, together with relaxation of the requirement of a ‘stepwise’ size structure, results in substantial improvements in fit over an existing sizing system. The proposed methodology enables the development of sizing systems that can either increase accommodation of the population, reduce the number of sizes in the system, or improve overall fit in accommodated individuals.