Improving 3D Printing Geometric Accuracy Using Design of Experiments on Process Parameters in Fused Filament Fabrication (FFF)
Bibliographic record
Abstract
This paper presents a statistical approach to improve the geometric accuracy of parts fabricated with a Fused Filament Fabrication (FFF), or also known as Fused Deposition Modelling (FDM) 3D printer. Design of Experiments (DoE) is used to develop the required statistical information and analyzing the experimental results. The paper provides an overview of the design process and the employed methodology including the design of the experiments, design of test samples, analyzing the experimental results, selection of the important process parameters, and the preliminary analyzes for the desired optimization of the process parameters. Customized design of the test samples made it possible to measure key features of the components quantifying the effect of the process parameters on the geometric accuracy resulting by the process. The outcome of the conducted Design of Experiments is a setting for selection of the process parameters to produce parts with considerably higher geometric quality. Variety of validation experiments are conducted and repeating the process under the selected parameters demonstrated that it is possible to achieve a desired level of reproducibility, using this 3D-Printing process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".