A methodology, which consists of design, optimization and evaluation of periodic lattice-based cellular structures fabricated by additive manufacturing, is presented. A user-friendly design framework for lattice cellular structures is developed by using a size optimization algorithm. A 3D modeling process for the lattice-based cellular structures is introduced for non-linear finite element analysis and production. The approach is demonstrated on compression block with periodic lattice-based unit cells. First, based on loading condition, most appropriate lattice layout is selected. Then, for the selected lattice layout, the lattice components are modeled as simple beam and size of the beam cross sections is optimized using in-house optimization approach for both yield and local buckling criteria. The 3D model for the optimized lattice structure is built and non-linear finite element study is conducted to predict the performance. Physical parts are 3D printed and tested to compare with the simulations. Material properties for the 3D printed parts are determined for the finite element study using reverse engineering of actual measured data.
The automotive industry has great interest in designing and producing lightweight high-performance components using fiber-reinforced polymers (FRPs), primarily due to their high specific strengths. Injection molding of FRP is one of the preferred processes to meet low-cost, high-volume objectives. It is imperative to account for shrinkage and warpage while designing the tools for injection molding. However, predicting shrinkage and warpage of injection-molded FRP parts remains a challenge. This is because both the structural and thermal properties depend on the condition of the fibers in the resin, that is, variation in the orientation, length, and concentration throughout the part. Additional challenges come from the fact that the material properties of polymers are a function of temperature, which varies as the parts cool. In this study, we are presenting a finite element-based semiempirical approach to address both these challenges and predict warpage due to cooling for a fiber-reinforced resin component in solid phase. The approach is demonstrated to predict warpage of an injection-molded flat plaque made of glass fiber-reinforced polypropylene, cooled from 160°C to room temperature of 23°C. First, the fiber orientation in the plaque is estimated. Next the material properties for the combined material, that is, glass and resin, are measured as a function of temperature. Then the combined material properties and calculated fiber orientations are used to estimate the ‘in-mold’ condition resin properties using reverse engineering. Finally, the warpage of the plaque is predicted using the estimated resin properties and fiber orientations. Warpage predictions using this method compare well with the measured experimental results. Our study demonstrates that valid predictions for shrinkage and warpage of injection-molded fiber-reinforced thermoplastic parts in solid phase can be made if accurate material properties are used.
A design framework that incorporates a size optimization algorithm is proposed for periodic lattice-based cellular structures fabricated by additive manufacturing. A 3D modeling process for the lattice-based cellular structures is integrated into the design framework for non-linear finite element analysis (FEA) and production. Material properties for the 3D printed parts are determined for the finite element study using reverse engineering of actual measured data. The lattice layout that will be used in the optimization is selected and the size of the cross sections is optimized using in-house optimization approach for both yield and local buckling criteria. The 3D model for the optimized lattice structure is built and non-linear finite element study is conducted to predict the performance. The approach is demonstrated on a compression block with periodic lattice-based unit cells. Physical parts are 3D printed and tested to compare with the simulations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.