Three Dimensional Pattern Grading Based on Deformable Body Features and 3D Developable Surface (Journal of Fiber Bioengineering and Informatics, 2018)
The garment pattern grading is a crucial procedure for manufacturing full sizes of clothing products. Thisprocedure traditionally starts with an ‘average’ pattern and generates a set of sized garment patterns byextending the ‘average’ pattern. The quality of the graded patterns depends on the grading technique andthe technician's experience. However, the garment patterns are often graded based on two-dimensionalrules 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 thispaper, we propose a new automatic approach of generating full sizes of garment patterns by flattening3D 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 HorizontalPiecewise B-spline Curves (HPBC) model. Three types of novel frames are developed from the HPBCmodel, namely feature frame, size frames, and ease frames. Based on these frames, a range of fit andflattenable 3D garments are established. Finally, the graded patterns can be generated by flatteningthese 3D garments.