Feature selection for constructing datasets toward automated lifecycle assessment for additive manufacturing
Bibliographic record
Abstract
Abstract Additive Manufacturing (AM) is considered an innovative technology to fabricate goods with green characteristics. In comparison to conventional manufacturing (CM) approaches, AM technologies have shown impressive 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 impacts of input variances. This knowledge barrier may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts in the product design and planning stages due to the iterative design-evaluation process. As such, the research community demands an automated LCA tool for supporting 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?”. A methodological framework for identifying the key influential parameters for AM is proposed. The framework was demonstrated by taking the fused filament fabrication (FFF) process as an example. Based on instantiation, LCA of over 200 AM instances, and correlation analysis, the key influential parameters are identified. Finally, a dataset with the identified features could be constructed. This dataset is expected to establish a common base for scale-up with joint efforts from the AM community.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".