Assembly dimensional variation modelling and optimization for the resin transfer moulding process

Abstract

The increasing demand for composite products to be affordable, net-shaped and efficiently assembled makes tight dimensional tolerance is critical. Due to lack of accurate process models, RTM dimensional analysis and control are often performed using trial-and-error approaches based on engineers' experiences or previous production data. Such approaches are limited to specific geometry and materials and often fail to achieve the required dimensional accuracy in the final products. This paper presents an innovative study on the dimensional variation prediction and control for polymer matrix fiber reinforced composites. A dimensional variation model was developed for process simulation based on thermal stress analysis and finite element analysis (FEA). This model was validated against the experimental data, the analytical solutions and the data from literature. Using the FEA-based dimensional variation model, the deformations of typical composite structures were studied and the regression-based dimensional variation model was developed. By introducing the material modification coefficient, this comprehensive model can account for various fiber/resin types and stacking sequences. The regression-based dimensional variation model can significantly reduce computation time by eliminating the complicated, time-consuming finite element meshing and material parameter defining process, which provides a quick design guide for composite products with reduced dimensional variations. The structural tree method (STM) was developed to compute the assembly deformation from the deformations of individual components, as well as the deformation of general shape composite components. The STM enables rapid dimensional variation analysis/synthesis for complex composite assemblies with the regression-based dimensional variation model. The exploring work presented in this research provides a foundation to develop practical and proactive dimensional control techniques for composite products.

A Review on the Mechanical Modeling of Composite Manufacturing Processes

“…Dong et al [ 150 ] developed a regression-based dimensional variation model to study the angled structure and single/multiple stiffener structures using a FE based model. The approach enabled to compute the deformation of composite assemblies by the introduced structural tree method.…”

Process Induced Defects in Liquid Molding Processes of Composites

“…Using the model, the authors developed an optimized curing cycle for a 25 mm-thick glass/polyester RTM composite, consisting of three consecutive dwell temperatures and dwell times and four specific heating/cooling rates. Applying this complex temperature profile resulted in a degree of cure of 99 %, an exothermic peak of 125 8C, and a cure cycle time of 250 s. Dong et al (2004), on the other hand, extended the cure numerical simulation of a T-shaped RTM glass/epoxy part to predict dimensional variation. The finite element simulation predicts temperature and degree of cure distributions throughout the composite part, allowing for strain prediction and subsequent residual stresses analysis.…”

“…Part distortion is inevitably induced by preform asymmetry, prevented in practice by the consistent use of symmetrical preform architectures. In symmetrical parts, distortions are observed to originate mainly from mold/part differential thermal expansion (Brauner et al, 2017;Dong et al, 2004;Golestanian and El-Gizawy, 2001). Other reported sources of distortion include recurrent resin rich zones and differential in-plane and the through-thickness cure shrinkage as well as differential thermal expansions of the composite constituents (Hsiao and Gangireddy, 2008;Khoun et al, 2011;Ruiz and Trochu, 2005).…”

“…Moreover, the cure cycle takes longer at low temperatures increasing the final manufacturing cost. Inversely, higher temperatures quicken the curing process but increase residual stresses, causing a higher defect occurrence (Dong et al, 2004;Hsiao and Gangireddy, 2008). Therefore, the process parameters need to be selected to balance conflicting goals of maximizing mechanical performance while minimizing cure induced defects, as well as cost, time and energy.…”

“…In addition to optimizing cure, researchers aim to reduce residual stresses and limit the occurrence of cure induced defects (Baran et al, 2017;Dong et al, 2004;Khoun and Hubert, 2010;Mostafa et al, 2017;Nielsen et al, 2013;Ruiz and Trochu, 2006). For instance, Ruiz and Trochu (2006) developed a multi-criteria optimization algorithm to optimize an objective function that incorporates conflicting goals such as minimization of residual stresses, maximization of the final degree of cure and reduction of cycle time, as well as avoiding thermal degradation of the matrix.…”