Set-based queries for multiscale shape-material modeling
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910685071409152 |
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| author | Igouchkine, Oleg Liu, Xingchen |
| author_facet | Igouchkine, Oleg Liu, Xingchen |
| contents | Multiscale structures are becoming increasingly prevalent in the field of mechanical design. The variety of fine-scale structures and their respective representations results in an interoperability challenge. To address this, a query-based API was recently proposed which allows different representations to be combined across the scales for multiscale structures modeling. The query-based approach is fully parallelizable and has a low memory footprint; however, this architecture requires repeated evaluation of the fine-scale structures locally for each individual query. While this overhead is manageable for simpler fine-scale structures such as parametric lattice structures, it is problematic for structures requiring non-trivial computations, such as Voronoi foam structures.
In this paper, we develop a set-based query that retains the compatibility and usability of the point-based query while leveraging locality between multiple point-based queries to provide a significant speedup and further decrease the memory consumption for common applications, including visualization and slicing for manufacturing planning. We first define the general set-based query that consolidates multiple point-based queries at arbitrary locations. We then implement specialized preprocessing methods for different types of fine-scale structures which are otherwise inefficient with the point-based query. Finally, we apply the set-based query to downstream applications such as ray-casting and slicing, increasing their performance by an order of magnitude. The overall improvements result in the generation and rendering of complex fine-scale structures such as Voronoi foams at interactive frame rates on the CPU. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Set-based queries for multiscale shape-material modeling Igouchkine, Oleg Liu, Xingchen Computational Engineering, Finance, and Science Graphics Multiscale structures are becoming increasingly prevalent in the field of mechanical design. The variety of fine-scale structures and their respective representations results in an interoperability challenge. To address this, a query-based API was recently proposed which allows different representations to be combined across the scales for multiscale structures modeling. The query-based approach is fully parallelizable and has a low memory footprint; however, this architecture requires repeated evaluation of the fine-scale structures locally for each individual query. While this overhead is manageable for simpler fine-scale structures such as parametric lattice structures, it is problematic for structures requiring non-trivial computations, such as Voronoi foam structures. In this paper, we develop a set-based query that retains the compatibility and usability of the point-based query while leveraging locality between multiple point-based queries to provide a significant speedup and further decrease the memory consumption for common applications, including visualization and slicing for manufacturing planning. We first define the general set-based query that consolidates multiple point-based queries at arbitrary locations. We then implement specialized preprocessing methods for different types of fine-scale structures which are otherwise inefficient with the point-based query. Finally, we apply the set-based query to downstream applications such as ray-casting and slicing, increasing their performance by an order of magnitude. The overall improvements result in the generation and rendering of complex fine-scale structures such as Voronoi foams at interactive frame rates on the CPU. |
| title | Set-based queries for multiscale shape-material modeling |
| topic | Computational Engineering, Finance, and Science Graphics |
| url | https://arxiv.org/abs/2411.02424 |