Electrical Transport in Tunably-Disordered Metamaterials
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917982758764544 |
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| author | Obrero, Caitlyn Tirfe, Mastawal Lee, Carmen Saptarshi, Sourabh Rock, Christopher Daniels, Karen E. Newhall, Katherine A. |
| author_facet | Obrero, Caitlyn Tirfe, Mastawal Lee, Carmen Saptarshi, Sourabh Rock, Christopher Daniels, Karen E. Newhall, Katherine A. |
| contents | Naturally occurring materials are often disordered, with their bulk properties being challenging to predict from the structure, due to the lack of underlying crystalline axes. In this paper, we develop a digital pipeline from algorithmically-created configurations with tunable disorder to 3D printed materials, as a tool to aid in the study of such materials, using electrical resistance as a test case. The designed material begins with a random point cloud that is iteratively evolved using Lloyd's algorithm to approach uniformity, with the points being connected via a Delaunay triangulation to form a disordered network metamaterial. Utilizing laser powder bed fusion additive manufacturing with stainless steel 17-4 PH and titanium alloy Ti-6Al-4V, we are able to experimentally measure the bulk electrical resistivity of the disordered network. The effective resistance of the structure calculated from the combinatorial weighted graph Laplacian is in good agreement with experimental data. However, the effective resistance is sensitive to anisotropy and global network topology, preventing a single network statistic or disorder characterization from predicting global resistivity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Electrical Transport in Tunably-Disordered Metamaterials Obrero, Caitlyn Tirfe, Mastawal Lee, Carmen Saptarshi, Sourabh Rock, Christopher Daniels, Karen E. Newhall, Katherine A. Disordered Systems and Neural Networks 82D30 Naturally occurring materials are often disordered, with their bulk properties being challenging to predict from the structure, due to the lack of underlying crystalline axes. In this paper, we develop a digital pipeline from algorithmically-created configurations with tunable disorder to 3D printed materials, as a tool to aid in the study of such materials, using electrical resistance as a test case. The designed material begins with a random point cloud that is iteratively evolved using Lloyd's algorithm to approach uniformity, with the points being connected via a Delaunay triangulation to form a disordered network metamaterial. Utilizing laser powder bed fusion additive manufacturing with stainless steel 17-4 PH and titanium alloy Ti-6Al-4V, we are able to experimentally measure the bulk electrical resistivity of the disordered network. The effective resistance of the structure calculated from the combinatorial weighted graph Laplacian is in good agreement with experimental data. However, the effective resistance is sensitive to anisotropy and global network topology, preventing a single network statistic or disorder characterization from predicting global resistivity. |
| title | Electrical Transport in Tunably-Disordered Metamaterials |
| topic | Disordered Systems and Neural Networks 82D30 |
| url | https://arxiv.org/abs/2410.11525 |