Glass Fiber Reinforced Acrylonitrile Butadiene Styrene Composite Gears by FDM 3D Printing
Bibliographic record
Abstract
Abstract 3D printing of gears via fused deposition modeling (FDM) has been recently introduced as a low‐cost efficient manufacturing method. Different materials have been 3D printed and Acrylonitrile Butadiene Styrene (ABS) with excellent mechanical properties has been found to be promising. However, 3D printed ABS gears possess a high level of abrasion rate. This paper introduces a new class of ABS‐based gears reinforced by different amounts of milled E‐glass fibers and 3D printed by FDM with acceptable thermo‐mechanical properties and performance. A set of thermo‐mechanical tests is carried out to provide an insight into the influence of adding glass fibers on the glass transition temperature (Tg), hardness and teeth bending strength, teeth failure force, weight lost, abrasion resistance, mechanical wear, and performance of composite gears. The mechanical behaviors of driving and driven gears are examined in high and room temperatures with or without lubrication. Microstructure and gear profile analysis of 3D printed layers, worn surfaces, and fracture locations are also conducted by SEM images and profile projector. The newly developed glass fiber reinforced ABS gears reveal a high level of thermo‐mechanical performance in terms of hardness, mechanical strength, bending force, abrasion and wear resistance compared to pure 3D printed ABS gears.
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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 | 0.001 |
| 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 | 0.001 |
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".