Stress-constrained topology optimization of heterogeneous lattice structures for additive manufacturing
Bibliographic record
Abstract
This study presents a topology optimization method for heterogeneous lattice structures subject to stress constraints. The proposed approach extends the ordered SIMP (Solid Isotropic Material with Penalization) framework to incorporate a composite material failure criterion. Specifically, a modified Tsai–Hill yield criterion is employed to characterize the anisotropic yielding behavior of the heterogeneous lattice, which is subsequently integrated into the optimization as a stress constraint. To address the variation in yield strength across different lattice configurations, a normalization strategy is applied to the stress field. Additionally, a P-norm aggregation scheme is introduced to efficiently handle local stress constraints while reducing computational cost. The equivalent elastic tensor and yield strength of each lattice configuration are obtained using a representative volume element (RVE) based on homogenization theory. The effectiveness of the proposed method is demonstrated through a series of 2D cases, achieving lightweight structural designs that satisfy stress constraints. Finally, full-scale mechanical analysis and 3D printing experimental validation further confirm the strength reinforcement of the optimized results.
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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 | 0.001 |
| 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) | 0.002 | 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".