Framework for The Generation and Design of Naturally Functionally Graded Lattice Structures

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Alzahrani, Mahmoud
Choi, Seung-Kyum
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Functionally Graded Lattice (FGL) Structures have shown improved performance over uniform lattice structures in different fields. Another form of functional grading can be seen in materials in nature, where the cellular structure can vary in both cell porosity and size. To distinguish between lattice structures that vary in porosity only and lattice structures that vary in both, we will refer to the latter in this research as Naturally Functionally Graded Lattice (NFGL) structures. Research into NFGL structures' performance against FGL structures in the literature is lacking. Furthermore, the current methods in the literature to generate these structures are severely limited and suffer from multiple drawbacks. This research aims to develop a framework, namely the NFGL Framework, to generate NFGL structures without the drawbacks that exist in current methods and to improve the performance of the generated structures using the NFGL Framework against existing FGL structures. The NFGL Framework uses a novel method to generate nodes for NFGL structures from using a developed simplified sphere packing algorithm to generate conformal NFGL structures in a deterministic and computationally efficient manner. Furthermore, the NFGL Framework can perform a similarity analysis using a modified Mean Structural Similarity (MSSIM) index to improve the performance of the generated NFGL structure. The generated structures using the NFGL Framework were tested against the existing methods and showed to overcome the drawbacks of these methods with improved performance and computational time. Furthermore, the generated NFGL structures were tested against FGL structures and the results showed a performance gain from the use of NFGL structures over FGL structures with a reduced computational cost.
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