An Investigation Into Fundamental Frequency Of Intact And Defective 3D-Printed PLA Beams
Bibliographic record
Abstract
3D printing manufacturing method gains significant attractions from industries due to its advantages over other production methods. Polylactic Acid (PLA), a biodegradable feedstock in 3D printing and one of the major filaments in the market, is investigated. Impulse Excitation Technique (IET) is used to obtain Young’s modulus of 3D-printed PLA specimens. The results are then compared with those achieved through experimental tensile testing. Displacement laser sensors, attached on the vertical base along cantilever 3D printed PLA cantilever beam samples, are used to capture the fundamental flexural frequencies of the beams, and are compared with those obtained from ANSYS FEM models. The effect of through-the-thickness cracks on the fundamental frequency of the defective samples is also studied, both experimentally and numerically. Samples including deliberately printed cracks of different depths and locations were examined through experiment and simulation. Results showed that the crack distance from the fixed-end has a more pronounced effect on fundamental frequency than the crack size. Finally, variation of numerically calculated fundamental frequency of defective beam models vs. crack depth and location are presented, which could be used as a database and pave the road to more elaborate crack identification techniques.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".