Crashworthiness investigations for 3D printed multi-layer multi-topology carbon fiber nylon lattice materials
Bibliographic record
Abstract
Cellular solids have superior energy absorption capabilities as compared to monolithic materials. Within this category of materials, lattice materials are of particular interest since their periodicity offers repeatable – and thus predictable – behavior. In combination with the advancements in additive manufacturing technologies, these lattice materials can be highly customized for a desired response. In this paper, the crashworthiness of unique multi-layer, multi-topology (MLMT) lattices is investigated. First, the nylon-carbon fiber composite material properties within a developed numerical model were tuned based on strut orientation. Then, the response of single-layer and three-layer cubic and octet lattices was investigated, where all lattices were designed with a relative density of 30%. Following the characterization of single-topology lattices, the response of MLMT lattices were investigated. Stress-strain, efficiency-strain, and multiple crashworthiness parameter data was collected for all lattices to facilitate in the comparison of those lattices. It was found that, experimentally, the unique MLMT lattices did not absorb more energy than their constituent layers combined, though modifications to the interface between layers could increase the energy absorption capability; the prediction of energy absorption of the MLMT lattices based on constituent layers was similar to actual numerical results. As all lattices were designed at the same relative density, the mass-specific energy absorption of the cubic-octet-cubic MLMT lattice (1.56 x10 3 J/kg) outperforms the single-topology octet lattice by 19% to 36% (1.15–1.31 x10 3 J/kg). While the octet-cubic-octet MLMT lattice (0.71 x10 3 J/kg) is outperformed by the single-topology cubic lattices (1.69–3.76 x10 3 J/kg), they see an increase of 59% to 77% in plateau stress (5.1–9.2 MPa) as compared to the MLMT lattice (2.1 MPa).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".