Identifying efficient solutions for additive manufacturing of short carbon-fiber reinforced polyamide 6 from energy and mechanical perspectives
Bibliographic record
Abstract
Additive manufacturing (AM) technologies have transformed manufacturing, by providing greater control over the material deposition. Owing to its versatility and high strength-to-mass ratio, AM with composite materials has grown exponentially, founding many applications in industries. Further implementation of AM with composite materials would require the user to be able to improve FFF (Fused Filament Fabrication) efficiency regarding energy and technical aspects. For this purpose, it is necessary to assess the energetic and mechanical effects of printing parameters. In this paper, five printing parameters are evaluated: bed heating strategy, bed temperature, nozzle temperature, layer thickness and deposition speed regarding energy consumption during printing phase and ultimate tensile strength (UTS). A decision-making tool based on Ashby's material selection tool is also implemented to further discriminate different solutions. Different weights and a Pareto front have been used to illustrate the methodology's potential. Among the tested parameters, bed heating strategy has proven to be the most impactful parameter while bed temperature shows little to no effect. This study proposes a methodology as starting point for efficient FFF printing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".