Multi-scale multi-material topology optimization for lattice structures with interface connective microstructures
Bibliographic record
Abstract
This article presents a novel concurrent multi-scale multi-material topology optimization method for designing lattice structures composed of solid microstructure and multiple lattice microstructures while the connectivity of dissimilar lattice microstructures is assured. At the macroscale, microstructures with different predefined volume fractions are treated as different materials. Lattice microstructure can be well connected with solid microstructure. However, the disconnection between different lattice microstructures would lead to load transition problems, impeding the practical applications of lattice structures. To address this issue, a new controllable interface identification method based on the pseudo-cost domain method is proposed. Identified interface domains between different lattice microstructures are filled with solid microstructures. A modified material interpolation framework is developed with the discrete material optimization method to achieve optimal topology of the macrostructure and the distribution of microstructures. This methodology is formulated as a compliance minimization problem. Numerical examples are provided to demonstrate the effectiveness of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".