Tuning structural dynamics through architected inner material designs: Numerical, experimental, and machine learning analysis
Bibliographic record
Abstract
Architected materials and structures have garnered significant interest due to their potential to furnish mechanical performances beyond the bounds of customary designs. The present work investigates the flexural and modal response of Triply Periodic Minimal Surface (TPMS)-based architected beam structures, engineered with different metamaterials, relative densities, and structural configurations. The work combines experimental and numerical analysis, performing 3-point bending tests on additively manufactured architected beams. Designs with exceptional flexural rigidity approaching 3 N . m 2 are identified, while insights into their inner stress profiles are provided. Moreover, their dynamics are assessed, revealing a topology-dependent modal response. The architected beam mechanics are compared with analytical and Finite Element Analysis (FEA) results for solid, non-architected elements, identifying alterations in the modal response, infeasible for solid beams. Evidence is provided that the appropriate microstructure selection allows for a controlled appearance of torsional and bending modes below 2000 Hz. Furthermore, machine learning models are developed to predict and explain the recorded performance, classifying the importance of underlying influential parameters. It is shown that the effective bending stiffness can be used as a lever to control the effective dynamic response. The analysis provides benchmark results for the engineering of advanced, lightweight structural members, with extraordinary dynamic attributes.
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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".