Experimental-Numerical Characterization of Fiber Reinforcements using micro CT Generated Digital Twins for Liquid Composite Molding Processes

  • Muhammad Ali

Student thesis: Doctoral Thesis


Liquid Composite Molding (LCM) is one of the widely used manufacturing techniques for high performance composite parts. A generic LCM process requires fiber reinforcements to be placed inside a mold cavity for subsequent resin injection. The fibrous reinforcements used in LCM exhibit dual-scale flow characteristics as they are often produced from a pre-existing assembly of fine reinforcing filaments, generally produced in the form of yarns or tows, which are then further combined together in various architectures. The complex and dual-scale architecture of the fibrous reinforcements, along with other process variables, may lead to local incomplete saturation and micro/macro-voids formation during resin impregnation. To manufacture high quality products via LCM processes, the flow characteristics of the resin and the resistance offered by the reinforcement to resin flow need to be examined. The overall goal of this study is to provide a single platform for faster compaction and permeability characterization of complex reinforcements, where conventional methods potentially require tedious and labor intensive experiments. A robust platform in the form of a hybrid experimental-numerical framework has been proposed and demonstrated for reinforcement characterization. In this hybrid approach, micro computed tomography (micro CT) images of different reinforcements at different levels of compaction were acquired through a non-destructive experimental setup using an in-situ compression fixture. The compaction response of the reinforcements was obtained from the load and displacement data acquired through the load cell of the compression fixture. The micro CT images were re-constructed to generate digital twins from which computational unit cells were extracted for numerical solutions of boundary value problems using governing equations of fluid dynamics. The flow field data from the numerical solution were used to compute the virtual preform permeability, which was then compared with experimental permeability measurements. Geometrical measurements taken from micro CT images were used to quantify variability within the reinforcement architecture. The method has been applied to various 2D and 3D reinforcement architectures as well as dry carbon fiber tape manufactured by automated fiber placement technique. As an alternative, an experimental-empirical approach is also formulated through which the numerical flow simulations can be completely avoided by making certain geometrical simplifications. Based on the analysis of the micro CT images, the procedure follows a hierarchical order, with a 'divide and conquer' strategy. The unit cell is sequentially divided until a homogenous region is obtained. The permeability of the homogenous regions is estimated using empirical models and then combined, in the order of division, to get an equivalent overall permeability. The results presented here highlight the versatility of the proposed hybrid characterization method over traditional experimental techniques. This work will be a milestone for the industry and academia to predict permeability without the need for conducting many lengthy and expensive experiments and will provide guidelines for any future benchmarking of virtual permeability measurements.
Date of AwardDec 2019
Original languageAmerican English


  • Compaction; Fibrous Reinforcements; Liquid Composite Molding; Permeability; X-ray Computed Tomography.

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