Three-dimensional fabric smoothness evaluation using point cloud data for enhanced quality control

Abstract

Assessing the smoothness appearance of fabrics, especially in three-dimensional forms, is vital for quality control. Existing methods often lack objectivity or fail to consider the full 3D structure of the fabric. In this study, we introduce an innovative system that harnesses point cloud data to overcome these limitations. We use a 3D scanning system to capture a multi-directional point cloud representation of the textile surface. The data undergoes stitching and filtering to obtain an optimized point cloud model for feature extraction. We propose the 3D and 2D alpha-shape area ratio as a novel feature parameter for determining surface smoothness. Validation was conducted with 730 point clouds from 146 fabric samples, achieving an impressive 95.81%, recognition accuracy, which aligns with expert subjective evaluations. This research not only presents a dependable method for 3D textile smoothness grading but also indicates its applicability in other industries where surface evaluation is pivotal.