Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing
Bibliographic record
Abstract
Additive manufacturing offers an immense potential for sustainability through optimized processes and material usage. This research investigates applications of machine learning models to predict and optimize key sustainability metrics in the energy consumption, part weight, scrap weight, and production time based on additive manufacturing process parameters such as the layer height, infill density, infill pattern, build orientation and number of shells. Four machine learning models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are evaluated with hyperparameter tuning performed using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno with Box constraints optimization algorithm which demonstrates a superior computational efficiency compared to traditional methods like the grid search and random search. Among the models, Random Forest achieves the highest accuracy, and the lowest Mean Squared Error for all target metrics. The results provide actionable insights into optimizing additive manufacturing processes for sustainability, and demonstrate that machine learning can directly link process parameters to environmental and economic impacts. This research bridges a critical gap in sustainable additive manufacturing by offering a computationally efficient and scalable optimization approach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".