Tribology Behavior of In-Situ FDM 3D Printed Glass Fibre-Reinforced Thermoplastic Composites
Bibliographic record
Abstract
Fused deposition modeling (FDM) 3D-printed parts are generally weaker compared to injection-moulded parts. Fibre reinforcement is one of the techniques used to enhance the mechanical strength and the tribological behavior of the FDM-printed parts. Recently, a new method for creating FDM 3D-printed composites was developed. Current work focuses on the tribological behavior of the glass fibre-reinforced PLA, manufactured using this new composite manufacturing method. Experiments were conducted to investigate the effect of Glass Fibre (GF) reinforcement on FDM 3D-printed thermoplastic composites, specifically polylactic acid (PLA) under different linear sliding speed and directions. All 3D printed glass fibre-reinforced PLA (PLA-GF) composites exhibited a lower wear rate and a higher friction coefficient compared to 3D printed PLA. Increasing in disc’s linear speed or sliding speed of the pins resulted in a lower coefficient of friction and wear rate. In addition, a perpendicular raster direction towards the disc rotation or pin motion experienced greater friction and greater wear.
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 | 0.000 |
| 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".