Characterization of Parametric Internal Structures for Components Built by Fused Deposition Modeling
Bibliographic record
Abstract
Parametric internal structures based on basic geometries present an easy and reliable way to reduce the internal solid volume fraction of the fused deposition modeling (FDM) rapid prototype (RP) part while guaranteeing the structural integrity of the physical model. This would reduce the material costs, which may be significant for large components. The present research proposes a novel method that, based on multidisciplinary and experimental approach, characterizes the mechanical behavior and predicts the material use and build time with the intention of optimizing the strength while reducing costs. The proposed approach comprises a set of physical and virtual experimentation techniques that merge to provide a comprehensive analytical resource. Results indicate that the FEA method represents accurately the mechanical behavior of the RP parts whereas the statistical analysis provides insight. This unique approach serves as a basis to understand the work performed in similar studies and leaves a number of possible topics that, once explored, will have a strong impact on the optimization of not only the FDM process, but the entire Rapid Prototyping industry.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".