Correction: Multiscale Design Optimization of Additively Manufactured Aerospace Structures Employing Functionally Graded Lattice Materials
Bibliographic record
Abstract
Periodic cellular solids offer an intuitive solution for lightweight designs as they provide the means to optimize the structure at multiple length scales generating novel geometries with maximized specific stiffness and strength properties. However, the generated designs are always characterized with various geometrical complexities rendering them impossible to build using conventional manufacturing techniques. With the advent and commercialization of additive manufacturing, the production of lightweight structures with periodic cellular topologies has begun to increase substantially as additive manufacturing largely relaxes the manufacturing constraints opening the design space for new boundaries that were not previously achievable. If serious applications of cellular solids are to be considered in aerospace, proper design procedures must be followed to guarantee parts that will meet stringent certification requirements imposed by certification authorities. This paper presents a new methodology for the design optimization of light and safe aerospace structures by combining topology, shape and lattice material optimization techniques. The procedure begins with a topology optimization process to generate a conceptual design. Sizing and shape optimization methods are then used to reduce stress concentrations and optimize the original design concept for even strain energy distribution. Remodeling is then required to obtain the first optimized design. Finally, multiscale design optimization employing lattice materials is conducted in the second stage where the output of the first stage design is divided into two domains, namely, a design space, for potential lattice structuring, and a non-design space, that is kept as solid material, particularly in areas of interface connections in the overall assembly. The lattice domains are meshed with equivalent beams representing the struts of a micro-truss lattice topology which is then employed in a stress and buckling constrained optimization for mass minimization. A case study is presented for the design optimization of a simple aircraft door hinge with results showing a reduction in mass of about 44% achievable employing the proposed optimization algorithm.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.034 |
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".