An Intelligent CAD-Based Framework for Sustainable Apparel Design

This paper describes an intelligent, CAD-based decision support and optimization framework for predicting fabric waste directly from geometry attributes of apparel pattern segments. It is applicable during the early design stage, providing a pre-screening alternative prior to the use of time-consuming nesting software. A synthetic database of 1000 apparel layouts was generated through a specifically developed Python algorithm, which creates realistic geometries of apparel pattern segments based on skirts, bodices and sleeves for a stipulated fabric width. Eight geometry attributes, with the ability to represent pattern fragmentation, pattern size, shape attributes, boundary complexity, and overall pattern variation were derived, and a random forest (RF) regressor was formulated to predict the essential nesting efficiency (NE) parameter. It was tested utilizing 500 decision trees, with 75% available features allocated for each split. Model performance was tested with 5-fold cross-validation (CV) which led to a coefficient of determination of 0.922, with an average prediction error of −0.0002. Validation of the method with layouts created with the Deepnest software demonstrated promising agreement between software-predicted and model-predicted values of fabric NE. The key influential features were revealed as total perimeter, convexity, number of pattern pieces and compactness, accounting for 90% of model information.