Micro- and Macro-Scale Topology Optimization of Multi-Material Functionally Graded Lattice Structures
Bibliographic record
Abstract
Lattice structures are becoming an increasingly attractive design approach for the most diverse engineering applications. This increase in popularity is mainly due to their high specific strength and stiffness, considerable heat dissipation, and relatively light weight, among many other advantages. Additive manufacturing techniques have made it possible to achieve greater flexibility and resolution, enabling more complex and better-performing lattice structures. Unrestricted material unit cell designs are often associated with high computational power and connectivity problems, and highly restricted lattice unit cell designs may not reach the optimal desired properties despite their lower computational cost. This work focuses on increasing the flexibility of a restricted unit cell design while achieving a lower computational cost. It is based on a two-scale concurrent optimization of the lattice structure, which involves simultaneously optimizing the topology at both the macro- and micro-scales to achieve an optimal topology. To ensure a continuous optimization approach, surrogate models are used to define material and geometrical properties. The elasticity tensors for a lattice unit cell are obtained using an energy-based homogenization method combined with voxelization. A multi-variable parameterization of the material unit cell is defined to allow for the synthesis of functionally graded lattice structures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".