Improved part strength for the fused deposition 3D printing technique by chemical modification of polylactic acid
Bibliographic record
Abstract
Fused deposition method (FDM) is popular as a plastic 3D printing technique. One of the drawbacks of this technology is its low bonding strength between layers, reducing through‐plane mechanical properties of a part compared with in‐plane strength within the layers themselves. This study focuses on altering the molecular structure of a polylactic acid by chain extension to increase the adhesion strength between layers, quantified by peel testing, in order to increase overall part strength. Four different samples were prepared in a high shear mixer, processed into filament and printed by a FDM‐type 3D printer. These samples were characterized for their thermal, rheological, mechanical and adhesion properties. The findings showed that the chain extender‐modified resins exhibited higher layer‐to‐layer adhesion strength as well as increased viscosity corresponding to an increasing degree of branching. The interlayer diffusion and entanglement of newly created branch chain ends improved bonding between the printed layers resulting in higher tensile properties. POLYM. ENG. SCI., 59:E59–E64, 2019. © 2018 Society of Plastics Engineers
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 | 0.000 |
| 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 | 0.001 |
| 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".