Mannequin fabrication methodology using 3D-scanning, modeling and printing

Abstract

PurposeThis paper presents a method to fabricate a fitting-mannequin using 3D-scanning, modeling and printing technologies.Design/methodology/approachScan data were obtained from 12 subjects with body size in the average range, selected from 208 women aged 20–29. The 3D-scan data were modified by selecting cross-sections from the cloud data, symmetrizing body shapes and obtaining mean points of body shapes. Fifteen spline curves, generated by connecting the mean points on the X–Y plane, were used as sketches and loft features to create the 3D mannequin models. A lower-body fitting mannequin was printed with polylactic acid plastic using a fused deposition-modeling 3D printer.FindingsThe cross-section circumference discrepancies among the 3D-printed mannequins in each step were within 1%, demonstrating the applicability and reliability of the 3D technologies proposed for mass customization.Originality/valueThe proposed methodology demonstrates the value of using 3D-scanning data to manufacture fitting-mannequins via mass customization. The study demonstrates the possibility and practicality of using 3D techniques to produce commercially viable fitting mannequins for the fashion industry.

Status of research on parametric methods for the reconstruction of 3D models of the human body for virtual fitting

“…Moreover, the data obtained from scanning contain gaps due to the problem of self-occlusion and thus need to be processed by skilled professionals. Although such methods have a high accuracy of modeling, they are time consuming and laborious (Liu et al, 2017;Oh and Suh, 2021;Lu et al, 2023). Parametric methods for reconstructing models of the human body that are based on images, the size of features and other parameters are efficient and convenient to use, and can naturally represent the shape and posture of the human body.…”

“…Although such methods have a high accuracy of modeling, they are time consuming and laborious (Liu et al. , 2017; Oh and Suh, 2021; Lu et al. , 2023).…”