Feature selection and framework design toward data-driven predictive sustainability assessment and optimization for additive manufacturing
Bibliographic record
Abstract
Additive manufacturing (AM) is considered an innovative technology to fabricate goods with green characteristics. In comparison to conventional manufacturing approaches, AM technologies have shown promising results in enhancing sustainability in production systems. Various research has been conducted to assess the environmental impacts of AM based on the well-known life cycle assessment (LCA) framework. However, this approach requires intensive domain knowledge to build the environmental impact model and interpret the findings. This knowledge barrier may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts. Such challenges can be particularly prevalent during the early product design and planning stages. As such, the research community demands an automated LCA tool to support AM toward elevated sustainability. To achieve this ambitious goal, this paper particularly investigates the fundamental question—“What are the key influential parameters that pose an impact on the environmental sustainability of AM?” Thus, this paper proposes a methodological framework for identifying the key influential parameters for AM. The framework was demonstrated by taking the fused filament fabrication process as a case study. Through instantiating various parts within the proposed framework and conducting LCA on over 200 AM instances, followed by correlation analysis, the key influential parameters were identified. Consequently, a data-driven predictive sustainability assessment and optimization framework was developed by integrating the identified influential features.
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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".