Dimension variation prediction for composites with finite element analysis and regression modeling

Abstract

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.