Penalization techniques for fatigue‐based topology optimizations of structures with embedded functionally graded lattice materials
Bibliographic record
Abstract
Abstract Topology optimization is widely used in industry to determine optimal load paths while satisfying stringent constraints. Recently, functionally graded lattice materials have been integrated into structural design for weight minimization, performance improvements, among others. One subject of interest is the long‐lasting designs with high fatigue failure resistance. The focus of this article is twofold, namely, to present readers with a step‐by step guide to derive sensitivities for various fatigue performances in gradient based topology optimization and to present and compare new and old methods for cumulative damage objectives. New formulations for cumulative damage assessment that modify local damage in topology optimizations by influencing local stress, yield strength or SN‐curve intercept are presented. Effects of functionally graded materials are also investigated to measure potential improvements in damage resistance. Methods of gradient descent such as moving asymptotes and sequential quadratic programming are included to compare solutions of fatigue minimization problems. The analysis shows that traditional method of stress penalization is the most effective to obtain satisfactory load paths for fatigue minimization when compared to yield and SN‐intercept penalization. Multiple benchmark tests provided results which showed slower convergence when yield strength penalization is activated which also required significantly more computation time than other methods.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".