Investigation of Printing Parameters of Additive Manufacturing Process for Sustainability Using Design of Experiments
Bibliographic record
Abstract
Abstract Additive Manufacturing (AM) offers many advantages to make objects compared to traditional subtractive manufacturing, for example, complex geometries can be easily fabricated, and light weight parts can be formed while maintaining the parts strength for the low carbon footprint, low material consumption and waste. But there are areas for AM to improve in sustainability, reliability, productivity, robustness, material diversity and part quality. Life cycle assessment (LCA) studies have identified that the AM printing stage has a big impact on the life cycle sustainability (LCS) of 3D printed products. AM building parameters can be properly selected to control the LCS. This research explores the optimal AM process parameters to reduce the process energy and material consumption. Investigated parameters include the printing layer height, number of shells, material infilling percentage, infilling type and building orientation. Design of experiments (DOE) approach and statistical analysis tools are used to find optimal parameter settings for sustainable AM. Models formulated in this research can be easily extended to other additive manufacturing processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".