Evaluating FDM Process Parameter Sensitive Mechanical Performance of Elastomers at Various Strain Rates of Loading
Bibliographic record
Abstract
To optimize the mechanical performance of fused deposition modelling (FDM) fabricated parts, it is necessary to evaluate the influence of process parameters on the resulting mechanical performance. The main focus of the study was to characterize the influence of the initial process parameters on the mechanical performance of thermoplastic polyurethane under a quasi-static and high strain rate (~2500 s−1). The effects of infill percentage, layer height, and raster orientation on the mechanical properties of an FDM-fabricated part were evaluated. At a quasi-static rate of loading, layer height was found to be the most significant factor (36.5% enhancement in tensile strength). As the layer height of the sample increased from 0.1 to 0.4 mm, the resulting tensile strength sample was decreased by 36.5%. At a high-strain rate of loading, infill percentage was found to be the most critical factor influencing the mechanical strength of the sample (12.4% enhancement of compressive strength at 100% as compared to 80% infill). Furthermore, statistical analysis revealed the presence of significant interactions between the input parameters. Finally, using an artificial neural networking approach, we evaluated a regression model that related the process parameters (input factors) to the resulting strength of the samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".