Page 23 - Anthropometry, Apparel Sizing and Design

18 Anthropometry, Apparel Sizing and Design

(f ) Digital library of fabrics, styles, accessories, closures, and garment sewing specifications
that can be cut and pasted to design garments, clo3D
(g) Tools to make technical drawings and do a tentative costing of the garment directly from a
sketch
(h) Visualize the fit of the garment in 3-D at the time of sketching (clo3D)
(i) Exceptional, easy-to-use interfaces for better communication between the client and the
designer

1.4.3 Applications of advanced patternmaking technologies
Shortcomings and inconsistencies in conventional patternmaking methods can be
addressed with the help of data provided by advanced technologies. A major application
area for the technological tools discussed earlier is in clothing used in protective, sports,
or medical fields (Ashdown et al., 2005; Daanen and Sung-Ae Hong, 2008; Hlaing et al.,
2011; Baytar et al., 2012; Maghrabi et al., 2015; Aluculese et al., 2016; Petrak et al.,
2016; Naglic et al., 2017; Bogovi cet al., 2018; Traumann et al., 2019). Some cases stud-
ies are presented in Chapters 12, 13, 15, and 16 of the book.
The process of making patterns on the avatar, known as 3-D/2-D/3-D, is demon-
strated in Fig. 1.6 for the design of motorcycle riders’ clothing. Garment is sketched
directly on the avatar (a); a mesh is generated (b), patterns are flattened into 2-D pat-
terns (c); 2-D patterns are assembled and sewn together and draped on the avatar for
virtual fit testing (d). If the avatar is animated, fit of the garment in various poses can
also be tested. The process can be used for MTM and RTW applications. Though the
patterns obtained by this method yield good fit, the pattern shapes obtained from flat-
tened mesh may not always conform to the accepted principles of fabric grain and dart
manipulation followed by patternmakers. This issue may have to be addressed by the
patternmaker manually (Mahnic et al., 2016).
Patterns generated directly from the body scan capture the peculiarities of shape or
posture of the customer. Aluculese et al. (2016) captured 3-D data of wheelchair-borne
women in different postures. The pose data were mapped onto human templates and

Fig. 1.6 Zoning for motorcycle rider.