Dimension variation prediction for composites with finite element analysis and regression modeling
Dimension variation prediction for composites with finite element analysis and regression modeling
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DOI：
10.1016/j.compositesa.2003.12.005
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发表时间：
2004-06
影响因子：
8.7
通讯作者：
C. Dong;Chuck Zhang;Zhiyong Liang;Ben Wang
中科院分区：
文献类型：
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作者：
C. Dong;Chuck Zhang;Zhiyong Liang;Ben Wang
本文提出了一种有效预测聚合物基纤维增强复合材料尺寸变化的新方法。基于有限元分析 (FEA) 的热应力预测，开发了尺寸变化模型。该模型根据实验数据、分析解决方案和文献数据进行了验证。使用基于有限元分析的尺寸变化模型，研究了典型复合材料结构的变形，并开发了基于回归的尺寸变化模型。通过引入材料改性系数，该综合模型可以考虑各种纤维/树脂类型和堆叠顺序。基于回归的尺寸变化模型可以消除复杂、耗时的有限元网格划分、材料参数定义和评估求解过程，从而显着减少计算时间，为减少尺寸变化的复合材料产品提供快速设计指南。结构树方法 (STM) 被开发来计算单个部件变形以及一般形状复合部件变形的装配尺寸变化。 STM 能够利用基于回归的尺寸变化模型对复杂复合材料组件进行快速尺寸变化分析/合成。本研究中提出的探索工作为开发复合材料产品实用且主动的尺寸控制技术奠定了基础。
This paper presents a new method for efficient prediction of dimension variations of polymer matrix fiber reinforced composites. A dimension variation model was developed based on thermal stress prediction with finite element analysis (FEA). This model was validated against experimental data, analytical solutions and the data from literature. Using the FEA-based dimension variation model, deformations of typical composite structures were studied and regression-based dimension variation models were developed. By introducing the material modification coefficient, this comprehensive model can account for various fiber/resin types and stacking sequences. The regression-based dimension variation model can significantly reduce computation time by eliminating the complicated, time-consuming finite element meshing, material parameter defining and evaluation solving process, which provides a quick design guide for composite products with reduced dimension variations. The structural tree method (STM) was developed to compute the assembly dimension variation from the deformations of individual components, as well as the deformation of general shape composite components. The STM enables rapid dimension variation analysis/synthesis for complex composite assemblies with the regression-based dimension variation models. The exploring work presented in this research provides a foundation to develop practical and proactive dimension control techniques for composite products.