NUMERICAL AND EXPERIMENTAL INVESTIGATION OF THE EFFECT OF DELAMINATION DEFECT AT MATERIALS OF POLYETHYLENE TEREPHTHALATE (PET)PRODUCED BY ADDITIVE MANUFACTURING ON FLEXURAL RESISTANCE
Bibliographic record
Abstract
Polyethylene terephthalate (PET) material, which is widely used in the packaging industry due to its thermal and mechanical properties, high chemical resistance, and low gas permeability, is among the most widely used polymer materials in the world. These properties have made their use in additive manufacturing methods widespread. Determining how some common additive manufacturing defects affect the products produced by these methods will increase the adoption of these technologies in the final product production. In this study, the investigation of the effect of layer non-joining defect called delamination on the impact strength of PET material produced by additive manufacturing method at different layer thicknesses was carried out experimentally and numerically. The effects to flexural stress on the artificially created layer adhesion defect on the middle layers of the parts produced and modeled with a layer thickness of 0.1/0.2/0.3mm were investigated. It has been observed that the increase in layer thickness decreases flexural strength. In addition, while the flexural strength of the specimens containing delamination decreased, the increase in layer thickness accelerated this decrease.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".