Strut-based design optimization for improving mechanical properties of lattice structures
Source abstract
Abstract
Abstract Polymer lattice structures, also known as polymeric cellular structures or polymeric foams, are widely used in various applications because of their unique properties, such as low density, high strength-to-weight ratio, and exceptional energy absorption. The objective of this work is to thoroughly examine the compression mechanical properties of strut-based truss constructions. As part of the study, these structures were created using an MSLA 3D printer, and both empirical and computational studies were conducted. Furthermore, the Taguchi method was employed for optimization purposes, and a thorough examination of statistical analyses was conducted. Lattice structures were developed using the SpaceClaim program and produced using the Ancubic M3 MSLA technology, which employs additive manufacturing. The LS-Dyna module of ANSYS Workbench was employed to create the finite element model of the lattice structures, and the manufactured specimens were subjected to compression experiments under the same conditions. The novelty of this work lies in generating MSLA 3D printer strut-based truss structures using both experimental and numerical analysis. Results show that increasing the cell counts also increases the compressive strength and absorbed energy. Similarly, struts and additional supports, which act synergistically, reduce stress concentration and improve stress distribution. Hence, compressive strength and absorbed energy increase. While structures consisting of pyramidical cells can be preferred in constructions where construction weight is not a limiting factor, it is preferable to use regular lattice structures in constructions where construction weight is a limiting factor.
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Research context
OpenAlex topics: Cellular and Composite Structures · Innovations in Concrete and Construction Materials · Structural Analysis and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (55)
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