A Dynamic Life Cycle Assessment Framework for Gas Turbine Package Components Using Sustainable Materials and Robotic Additive Manufacturing
Bibliographic record
Abstract
Abstract The industrial sector accounts for 30% of global greenhouse gas emissions and 37% of global energy consumption, necessitating sustainable solutions in heavy manufacturing. This research explores the use of sustainable materials and Additive manufacturing (AM) for gas turbine (GT) package components, specifically the baseplate and air filter module. Traditional steel-based components are replaced with wood-plastic composites and eggshell-filled concrete, produced through robotic AM. A Life Cycle Assessment (LCA) was conducted using OpenLCA and secondary data from the Ecoinvent database, employing a cradle-to-gate framework to evaluate Global Warming Potential (GWP), resource depletion, and toxicity. Initial results indicate a 40% reduction in GWP compared to steel-based components Building on these findings, a dynamic unit process model for Fused Deposition Modeling (FDM) is introduced. Unlike static LCA models, this approach uses time resolved energy consumption and material flow data to enable scenario-based impact assessments. Python-based tools further streamline LCA workflows, allowing for rapid recalculation of results when print parameters or materials change. By integrating LCA into the early design phase, this research demonstrates a pathway toward more environmentally responsible manufacturing and offers a scalable framework adaptable to a wide range of AM 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".