Topological optimization design of aircraft landing gear door hinge frame
Bibliographic record
Abstract
To achieve lightweighting of the aircraft landing gear door hinge under opening and closing loads, a topological optimization model based on the variable density method was established, with structural stiffness as the constraint and minimum mass as the objective. The hinge was redesigned according to the optimized configuration, and the stiffness and strength of the design area were validated. The original aluminum alloy hinge material was replaced with lower-density polyetheretherketone (PEEK), which can be fabricated into complex structures via fused deposition modeling (FDM), thereby enhancing design freedom for topology optimization. However, FDM-printed PEEK's mechanical properties are influenced by printing parameters. This study conducted tensile tests on PEEK specimens printed with different FDM parameters (e.g., layer height, platform temperature, and infill pattern). The optimal printing parameters were determined as 0.1 mm layer height, 120 °C platform temperature, and tetrahedral infill. Subsequently, the best-performing specimens underwent heat treatment, and the effects of different annealing parameters on tensile strength were investigated. The results showed that annealing at 330 °C for 2 hours yielded the highest strength improvement. Furthermore, the hinge's loading conditions during door operation were simulated via multi-body dynamics analysis, while static simulations under peak loads identified stress concentration areas. Topology optimization was then performed to minimize material usage while maintaining mechanical performance, achieving the lightweight goal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".