Global sensitivity analysis reduces data collection efforts in LCA: A comparison between two additive manufacturing technologies
Bibliographic record
Abstract
Accounting for the environmental impacts in the design of technologies is becoming a necessity for manufacturers. Life cycle assessment (LCA) is a well-established method to quantify the environmental impacts of products and services through a holistic perspective and is increasingly used to support the eco-design of products and technologies. However, LCA generally faces an inherent issue with data availability. Given the constraints on both time and cost for collecting inventory data to feed the LCA model, a trade-off between data cost robustness is required with an efficient data collection strategy. The objective of this study is to develop a framework to prioritize data collection efforts in LCA using uncertainty analysis. This starts with a screening life cycle inventory analysis systematically informing all input parameters with uncertainty ranges. Monte Carlo analysis is then used to propagate the uncertainty through the model. Stochastic results are then compared with an acceptable confidence level set by the decision maker. This is followed by a global sensitivity analysis using Sobol' indices to rank different input parameters based on their contribution to the variability of the results. This paves the way for an iterative process prioritizing further data collection focusing on the most sensitive parameters. A case study comparing cold spray and wire arc additive manufacturing illustrates how to operationalize the framework. Learnings from the case study highlight the importance of defining the uncertainty ranges and the convergence criterion, where more work is needed in that domain.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".