Optimization of Process Parameters of Fused Filament Fabrication of Polylactic Acid Composites Reinforced by Aluminum Using Taguchi Approach
Bibliographic record
Abstract
The benefits of the fused filament fabrication (FFF) method, including its simplicity, affordability, and accessibility, have made it the most commonly used additive manufacturing technique. Polylactic acid (PLA) is the most widely used material in FFF, but its use has been limited by low mechanical properties and a small processing window. To address this, PLA composites are used to improve its properties. Correlating mechanical properties with process parameters is crucial for producing high-quality composite parts. This study investigated the effects of material and process parameters on mechanical properties, such as tensile strength and elongation-at-break, using a customized Delta Rostock FFF printer. Two types of filaments were used, pure PLA and PLA/Aluminum composites. Printing speed (10, 20, and 30 mm/s) and raster angle (0/90, −45/45, and −30/60) were selected as process input parameters. The Taguchi method was used for the experiment design, and signal-to-noise ratio analysis was used for statistical optimization. The optimal values for achieving maximum tensile strength of 61.85 MPa and maximum elongation-at-break of 17.7% were determined. Furthermore, the signal-to-noise ratio analysis indicated that the filament type had the greatest influence on the tensile strength, whereas printing speed had the greatest impact on the elongation-at-break.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".