Study on UGNX-LCA Integration for Sustainable Product Development
Bibliographic record
Abstract
It is beneficial to conduct LCA(Life Cycle Assessment) during early stages of product development, as the earlier the environmental problems associated with the product life cycle are discovered, the less costly and more effective the preventing measures are. However, due to the lack of data communication tools between CAD and LCA systems, life cycle data collection during design stage is difficult. This paper presents a feature-based method of UGNX-LCA integration for sustainable product development. A feature-based multi-view life cycle model for integrating product-process-LCI (Life Cycle Inventory) data is developed based on mapping mechanism between engineering domains of product design, process planning and LCA. Data migration from UGNX models to LCA, including UG modeling feature identification, UG-LC(Life Cycle) feature transformation and LC feature model output are realized by embedded integrator. A case study of data migration from UGNX to LCA is presented to demonstrate the proposed approach.
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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".