Xiang He, Jiang Yaming, Zhou Yiying, Malengier Benny, Van Langenhove Lieva
Ministry of Education Key Laboratory of Advanced Textile Composite Materials, Institute of Composite Materials, Tiangong University, Tianjin 300387, China.
School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.
Polymers (Basel). 2022 Apr 25;14(9):1742. doi: 10.3390/polym14091742.
The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components' surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes.
纤维增强复合材料的力学性能高度依赖于局部纤维取向。在本研究中,构建了一种用于复合材料部件表面的低成本纱线取向重建方法,利用双目结构光检测技术实现复合材料表面纤维取向的有效检测。使用电动转台能够快速获取无盲点的回转体形状样本。四次采集操作可完全覆盖样本表面,轨迹识别覆盖率达到80%,且纱线空间偏差的人工验证与自动化技术显示出良好的一致性。结果表明,基于所提方法开发的系统能够实现不同角度视图纱线路径的自动识别,在实际制造过程中基本满足质量控制标准。