Bragg Bands Generation in Beams and Plates for Mass Reduction and Vibroacoustic Performance
Bibliographic record
Abstract
Most of the existing approaches for designing locally resonant metamaterials involve addition or inclusion of elements such as tuned mass dampers or masses following a periodic pattern. The primary focus of this study is on the generation of Bragg bands in aluminum cantilever beams and plates following a subtraction-based approach. Periodic cells are created by using a simple machining operation, that is periodically removing material in the thickness direction. The goal is to design structures that are lighter than their homogeneous versions, but that can nevertheless exhibit similar or improved vibroacoustic behavior. Numerical simulations are first considered. Regarding beams, the effects of the number of periodic cells along length and the thickness removal are studied for a fixed beam length. For plates, the global vibration behavior as well as the sound transmission loss are evaluated for two plates with periodic diagonal material removal. Contact less vibration measurements using a multi-point laser vibrometer and an automatic impact hammer are then performed for beams and plates. Sound transmission loss measurements are also conducted for an homogenous aluminum plate, and two machined plates with two different diagonal periodicities for material removal. The obtained results generally indicate that simple machining operations can be a simple but efficient way for the generation of band gaps in beam-like and plate-like lightened structures.
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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 | 0.000 |
| 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".