Open Access

ARTICLE

Fast Mold Filling Simulation Based on the Geodesic Distance Calculation Algorithm for Liquid Composite Molding Processes

by J. Wang¹, P. Simacek¹, S.G. Advani¹

1 Department of Mechanical Engineering, University of Delaware, Newark, DE, USA.
2 Center for Composite Materials, University of Delaware, Newark, DE, USA.
3 Corresponding author E-mail: advani@udel.edu

Computer Modeling in Engineering & Sciences 2015, 107(1), 59-79. https://doi.org/10.3970/cmes.2015.107.059

Abstract

In Liquid Composite Molding (LCM) processes, resin is introduced into a stationary fiber reinforcement placed in the mold, until the reinforcement gets fully saturated with resin and all volatiles are vented out of the part. Finite element based software packages have been developed to simulate the mold filling process and eliminate expensive and tedious trial and error practices to arrive at a successful mold filling without any voids. However, the non-homogeneity of the fiber reinforcement material and its placement and layup in the mold creates a large degree of variability of flow patterns during the resin impregnation process. Executing simulations for every possible permutation of flow scenarios, which is required to devise a robust process design is computationally expensive. Therefore, it is necessary to find faster approximate mold filling simulation methods so that all simulations can be performed within a reasonable time frame.
In this paper, a discretized one-dimensional flow model is developed to predict the fill time based on the distance resin travels. Combined with Dijkstra’s algorithm, this model is then implemented on spatial surface meshes to calculate fill time for each node and generate flow development pattern. The computational model developed can predict the mold filling pattern for complex parts even with variable permeability or thickness of the fiber preform, and can capture the disturbed flow behavior along any difficult geometric features at a fraction of the computational cost. Case studies are presented to demonstrate the efficiency and accuracy of the distance-based model.

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