(untitled) (Journal of Fiber Bioengineering and Informatics, 2018)
The garment pattern grading is a crucial procedure for manufacturing full sizes of clothing products. This procedure traditionally starts with an ‘average’ pattern and generates a set of sized garment patterns by extending the ‘average’ pattern. The quality of the graded patterns depends on the grading technique and the technician's experience. However, the garment patterns are often graded based on two-dimensional rules that hardly provide an accurate fit because of shape variations and complexity of 3D human bodies. Additionally, the traditional pattern grading is conducted manually and very time-consuming. In this paper, we propose a new automatic approach of generating full sizes of garment patterns by flattening 3D garments created from parameterized mannequins in fulfilling the requirements of body structures, sizing chart and garment fit. Firstly, a parametric human body model is introduced called Horizontal Piecewise B-spline Curves (HPBC) model. Three types of novel frames are developed from the HPBC model, namely feature frame, size frames, and ease frames. Based on these frames, a range of fit and flattenable 3D garments are established. Finally, the graded patterns can be generated by flattening these 3D garments.