Analysis of Composite Structures in Curing Process for Shape Deformations and Shear Stress: Basis for Advanced Optimization

Identifying residual stresses and the distortions in composite structures during the curing process plays a vital role in coming up with necessary compensations in the dimensions of mold or prototypes and having precise and optimized parts for the manufacturing and assembly of composite structures. This paper presents an investigation into process-induced shape deformations in composite parts and structures, as well as a comparison of the analysis results to finalize design parameters with a minimum of deformation. A Latin hypercube sampling (LHS) method was used to generate the required random points of the input variables. These variables were then executed with the Ansys Composite Cure Simulation (ACCS) tool, which is an advanced tool used to find stress and distortion values using a three-step analysis, including Ansys Composite PrepPost, transient thermal analysis, and static structural analysis. The deformation results were further utilized to find an optimum design to manufacture a complex composite structure with the compensated dimensions. The simulation results of the ACCS tool are expected to be used by common optimization techniques to finalize a prototype design so that it can reduce common manufacturing errors like warpage, spring-in, and distortion.

Automated summary

Identifying residual stresses and distortions in composite structures during the curing process is crucial for making necessary compensations in mold or prototype dimensions and achieving precise and optimized parts for manufacturing and assembly. A study was conducted to investigate process-induced shape deformations in composite parts and structures, with analysis results compared to finalize design parameters with minimal deformation. The Latin hypercube sampling method was used to generate random points of input variables, which were then analyzed with the Ansys Composite Cure Simulation tool to find stress and distortion values, ultimately reducing common manufacturing errors.