Abstract

In this paper, the problem of inferring fabric mechanical properties based on three-dimensional (3D) fabric drape model and fabric weight was studied. Firstly, the three-dimensional point cloud of draped fabric was scanned via a self-built three-dimensional scanning device followed with triangulation. Then the local linear embedding (LLE) algorithm was used to resample the three-dimensional draped models (triangular meshes). Besides, all meshes resampled with the same uniform discrete points have the same triangular topology (vertex number and triangle index). Thirdly, the distribution function of curvature (DFC) of resampled 3 D mesh was extracted. At last, four neural networks with the combination of fabric weight and DFC as input were constructed to predict the bending rigidity and shearing stiffness separately. The result shows that the correlation coefficient between true warp bending rigidity and predicted result could reach 0.925. The correlation coefficient between true warp shearing stiffness and the predicted result is 0.7953. The correlation coefficient between true weft bending rigidity and the predicted result is 0.857. The correlation coefficient between true weft shearing stiffness and the predicted result is 0.605.

摘要

本文研究了基于三维（3D）织物悬垂模型和织物重量推断织物力学性能的问题。首先，通过对悬垂织物的三维点云进行扫描一个自建的三维扫描设备，然后进行三角测量。然后使用局部线性嵌入 (LLE) 算法对三维覆盖模型（三角形网格）进行重新采样。此外，使用相同的均匀离散点重采样的所有网格都具有相同的三角形拓扑（顶点数和三角形索引）。第三，提取重采样3D网格的曲率分布函数（DFC）。最后，构建了四个以织物重量和DFC相结合的神经网络作为输入，分别预测抗弯刚度和抗剪刚度。结果表明，真经向弯曲刚度与预测结果的相关系数可达0.925。真经剪刚度与预测结果的相关系数为0.7953。真纬向弯曲刚度与预测结果的相关系数为0.857。真实纬纱剪切刚度与预测结果的相关系数为0.605。