Extperimental Characterization of 3D Printed Thermoplastic Plates Subjected To Low Velocity Impact
Bibliographic record
Abstract
Poly Lactic Acid (PLA) is a biodegradable material which is being extensively used in industrial applications. Due to its low glass transition temperature and cost, PLA is ideal as a feed stock in 3D printing applications. However, it has a brittle nature which makes it vulnerable to impact loads. In this paper, PLA is used to make 3D printed plates that are impact tested using an in-house low velocity impact test apparatus. A high-speed camera and an infrared thermography system are used to investigate the impact damage properties of the material. The plates manufactured with 0° orientation are used to conduct two different experiments; one with varying energies and the other with varying thickness at two different impact locations, namely at plate’s centre and close to a clamped edge. At 1 J impact energy, the plates showed a tensile crack behaviour (cracks between extrudates) and for 3 J energy it showed a mixed crack behaviour of tensile and shear (cracks along and across extrudates) with more energy dissipations than the 1 J impact. For the 1 J impact, more energy is dissipated at the centre of the plate (42.3%) than the impact close to a clamped edge (32.8%), whereas for the 3 J impact more energy is dissipated near clamped edges (97.1%) compared to the centre of the plate (54.9%). Subsequently, the 3 J impact is used for the second experiment due to the higher energy dissipation. Finally, an experimental study is conducted on plates with varied layer thickness from 0.10 mm to 0.18 mm. Results show that the increase in layer thickness (decrease in number of layers) increases the impact absorption for plates impacted at their centre. For plates impacted near their clamped edge, a zig-zag impact damage pattern of increasing and decreasing magnitudes is observed, but the energy dissipation values are higher than the centre impacted plates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".