A heterogeneous lattice structure modeling technique supported by multiquadric radial basis function networks
Bibliographic record
Abstract
ABSTRACT With the rapid advancement of the multimaterial additive manufacturing (AM) technology, the heterogeneous lattice structures (HLSs) comprising the multiphase materials with gradual variations have become feasible and accessible to the industry. However, the multimaterial AM capabilities have far outpaced the modeling capability of design systems to model and thus design novel HLSs. To further expand the design space for the utilization of AM technology, this paper proposes a method for modeling HLS with complex geometries and smooth material transitions. The geometric modeling and material modeling problems are formulated in a rigorous and computationally effective manner. The geometric complexity of HLS is significantly reduced by a semi-analytical unit cell decomposition strategy that is applied to split HLS into material units: struts and connectors. The smooth material transitions of the connector associated with multimaterial struts are realized by interpolating the discrete material property values defined at control points using a multiquadric radial basis function network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".