Design optimization and 3D printing of a sandwich panel for a lunar roverframe
Bibliographic record
Abstract
A novel 2D cellular material optimization method is introduced in this study to optimize the stiffness of a lightweight sandwich panel by changing the cellular configuration of the core. The literature presents computer expensive optimization methods that are not necessarily adapted to the manufacturing method. Here, the stiffness of a sandwich panel is determined by the finite element method and a blackbox optimizer determines an optimal cellular configuration, which are adapted to fused filament fabrication. This optimization method is combined with a parametric study to investigate the influence of core thickness, skins thickness, cellular walls width and cellular density. The optimized sandwich panel and a honeycomb reference are 3D printed and mechanically tested and compared to validate the optimization method. This optimization method is also used on the frame of a lunar rover to decrease its weight and increase its stiffness, thus increasing the frequency of the first mode of the rover in the launcher. Since the lunar rover is a large component, a special 3D printer is required to manufacture it. We developed a custom 3D printer, consisting of a 6 degrees-of-freedom robotic arm mounted by a Typhoon, a high-flow filament based printhead. This high-flow extruder allows to print the frame of a lunar rover within a day, compared to two weeks using a conventional printhead. The printing bed and an enclosure allow to print different materials, from PLA to reinforced PEEK, with dimensions of 1.5m 0.8m. Using this 6 degrees-of-freedom robot, we also developed a non-planar slicer with variable layer height and a control of the nozzle's orientation. This allows to minimize the use of supports, to benefit from the anisotropic behavior of fused filament fabrication, and to smoothen surface roughness. This slicer allows to maximize the contact area between layers and to avoid surface scratching on non-planar trajectories due to geometric incompatibilities between the nozzle and the 3D printed part.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".