Digital volume correlation analysis of polylactic acid based fused filament fabrication printed composites
Bibliographic record
Abstract
Fused filament fabrication (FFF) has rapidly begun to see implementation in industrial fields as a method of rapid manufacturing. Traditional FFF parts are made from a single thermoplastic polymer. The polymer is heated to its melting point and deposited on a work bed where a model is gradually built from the base up. While traditional FFF parts have low mechanical properties, a reinforcing phase allows for improved mechanical properties. The addition of a reinforcing material to the base polymer and complex internal microstructure of the 3 D printed party leads to anisotropic mechanical properties. Thus, these materials' mechanical properties become challenging to characterize using traditional measurement techniques due to the previously mentioned factors. Therefore, it is essential to develop a method in which mechanical properties can be measured and analyzed. This study aims to characterize the mechanical behaviour under a uniaxial tensile load of an FFF produced polylactic acid (PLA)-copper particulate composite. The internal response of the FFF sample was imaged using micro-computed tomography at predetermined loads. The μ-CT images were input into an open-source digital volume correlation (DVC) software to measure the internal displacements and strain tensor fields. The study results show the development of different strain fields and interior features of the FFF parts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".