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The ability to manufacture complex design geometries via Additive Manufacturing (AM) has led to a rapid growth in advancing the design methods, fabrication, and application of Triply Periodic Minimal Surface (TPMS) lattices with minimal surface topologies. Due to its zero-mean curvature, TPMS lattices can be additively manufactured without any sacrificial support structures and offer both design and manufacturing engineers, unprecedented control over the local physical properties (surface area, relative density, etc.) and local mechanical properties (flexural strength, Young’s modulus, etc.). TPMS lattices are of high interest for a wide range of applications such as biomedical implants, energy absorption, and surface fluidic applications such as heat exchangers, and energy storage. Recent advancements in functionally graded TPMS lattice design by varying local lattice geometry has shown to result in different mechanical performance. However, there have been limited studies in understanding the functional grading of AM process conditions (e.g., Laser-Powder Bed Fusion in this study) and lattice sheet thickness to better map the design-processing conditions-properties. The goal of this study is to achieve similar mechanical properties in TPMS sheet lattices with two different TPMS sheet thicknesses by varying laser processing conditions (e.g., contour and hatch conditions in this study). Quasi-static tensile testing of solid samples with corresponding AM conditions and 3-point bending tests of TPMS lattices were performed in accordance with ASTM E8 and ASTM E290, respectively. It was observed that the flexural properties of the 0.75 mm and 0.25 mm TPMS lattices are similar and exhibit different properties with different scan strategies and speed variations under contour-only and hatch-only laser scanning strategies. Also, the 0.75 mm TPMS sheet lattices exhibited 79 % higher flexural stiffness than the 0.25 mm sheet lattices. It was also observed that this observed trend was reversed in the case of tensile properties. Findings from this study can provide new directions towards achieving gradient TPMS lattice designs with varying local mechanical performance by grading the laser scanning strategies to achieve desired mechanical properties and surface topologies.
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Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks
This paper investigates the structure–property relations of thin-walled lattices, characterized by their cross-sections and heights, under dynamic longitudinal compression. These relations elucidate the inter- actions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design system to generate different lattice designs and used the finite element method to simulate their response with the Johnson–Cook material model. Using an autoencoder, we encoded the cross-sectional images of the lattices into latent design feature vectors, which were supplied to the neural network model to generate predictions. The trained models can accu- rately predict lattice energy absorption curves in the key-based design system and can be extended to new designs outside of the system via transfer learning.
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- Award ID(s):
- 1926353
- PAR ID:
- 10537696
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Computers & Structures
- Volume:
- 277-278
- Issue:
- C
- ISSN:
- 0045-7949
- Page Range / eLocation ID:
- 106940
- Subject(s) / Keyword(s):
- Thin-walled lattices Structure–property relations Johnson–Cook model Neural networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.compstruc.2022.106940
