Open Access Publications

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Open access publications by faculty, staff, postdocs, and graduate students from the Center for Composite Materials.


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    Efficient numerical modeling of liquid infusion into a porous medium partitioned by impermeable perforated interlayers
    (International Journal for Numerical Methods in Engineering, 2022-11-02) Moretti, Laure; Simacek, Pavel; Advani, Suresh G.
    Numerical modeling of flow through porous media and the simulation of liquid flow through orifices, channels and perforated walls, membranes, interlayers find applications in various fields. However, the mesh refinement needed to describe the detail at the scale of orifices within a domain multiple orders of magnitude larger raises numerical challenges. The present work proposes a pragmatic solution to model perforated layers partitioning a large porous media domain using 1D elements to model the holes and connect the 3D elements which represent the porous media. As an illustration, the approach is applied in liquid composite molding processes, and to the processing of large thick panels toughened with perforated interlayers. However, this work could be adopted in numerous fields. The combination of 3D and 1D elements to manage components with different dimensions has been used before, however no proper analysis of the loss of accuracy introduced has been conducted to our knowledge. A systematic parametric study is conducted to quantify the impact of the length of the domain, the number of interlayers, the diameter of the holes and the viscosity of the fluid on the loss of accuracy. Meshing rules and directions are provided to improve the accuracy of the simulations.
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    A High-Consolidation Electron Beam-Curing Process for Manufacturing Three-Dimensional Advanced Thermoset Composites
    (Journal of Manufacturing Science and Engineering, 2022-07-27) Rizzolo, Robert H.; Walczyk, Daniel F.; Montoney, Daniel; Simacek, Pavel; Mahbub, Md Rashef
    This paper describes the application of a new manufacturing process for low-cost and rapid consolidation and curing of advanced thermoset composites that avoids the use of expensive prepreg, autoclaving, and thermally induced curing. The process, called VIPE, uses a novel tooling design that combines vacuum infusion (VI) of a dry preform with resin, a rigidly backed pressure focusing layer (P) made of an elastomer to consolidate the wet preform with uniform pressure, and high-energy electron beam curing (E). A VIPE tool is engineered and fabricated to manufacture 3D laminate bicycle seats composed of woven carbon fiber textile and an electron beam-curable epoxy acrylate. Details of the tooling design discussed include computational fluid dynamics (CFD) simulation of the vacuum infusion, iterative structural finite element analysis (FEA) to synthesize the pressure focusing layer (PFL), structural FEA to design the top mold made of a composite sandwich structure for electron beam transparency, and Monte Carlo electron absorption simulations to specify the e-beam energy level. Ten parts are fabricated using the matched tool (bottom aluminum mold covered with silicone layer and top mold with carbon/epoxy skins separated by foam core) after the dry textile preform contained within is infused with resin, the tool halves are clamped under load, and a 3.0 MeV e-beam machine bombards the tool for less than 1 min. Part thickness, part stiffness, surface roughness, and fiber and void volume fractions measurements show that aerospace quality parts with low cycle times are achievable, although there is high variability due to the small number of replicates and need for process optimization.
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    Theory-guided machine learning for optimal autoclave co-curing of sandwich composite structures
    (Polymer Composites, 2022-07-06) Lavaggi, Tania; Samizadeh, Mina; Niknafs Kermani, Navid; Khalili, Mohammad Mahdi; Advani, Suresh G.
    The bonding of a honeycomb core to the thermoset prepreg facesheets by co-curing them allows one to manufacture composite sandwich structures in a single operation. However, the process is strongly dependent on the prescribed autoclave cure cycle. A previously developed physics-based simulation can predict the bond quality as a function of the process parameters. The disadvantage of physics-based simulations is the high computational effort needed to identify the optimal cure cycle to fabricate sandwich structures with desired bond-line properties. Theory guided machine learning (TGML) methods have demonstrated their capabilities to reduce the computational effort for different applications. In this work, three TGML models are trained on a data set produced from physics-based simulations to predict the co-cure process of honeycomb sandwich structures. The accuracy of the TGML models were compared to select the best performing predictive tool. In addition to reduction of computational time by orders of magnitude, we demonstrate how the TGML tools can also quantify the contribution of each process parameter on the properties of the fabricated part. The most accurate model was implemented in an optimization routine to tune the input process parameters to obtain the desired properties such as the bond-line porosity and facesheet consolidation level. This methodology could be extended to any process simulation of composites manufacturing processes.
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