Automatic generation of parametric patterns from grading patterns using artificial intelligence

Abstract

Purpose

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

Design/methodology/approach

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

Findings

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

Research limitations/implications

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

Originality/value

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

Citation

Oh, J. and Kim, S. (2023), "Automatic generation of parametric patterns from grading patterns using artificial intelligence", International Journal of Clothing Science and Technology, Vol. 35 No. 6, pp. 889-903. https://doi.org/10.1108/IJCST-07-2022-0102