Integrated Top Down Dynamic and Hybrid Life Cycle Analysis Based Sustainable Design Approaches for New Product Development
Bibliographic record
Abstract
Environment friendly design methodology is a valid trade-off between the degrees of freedom and the sustainable solution. Life Cycle Assessment (LCA) is a powerful tool to calculate the environmental impact from the product and the manufacturing systems from cradle to grave including the consumption of all types of the resources. However, complexity of the LCA restricts its usefulness in the current state-of-the-art product and process system development. Most often, companies ignore to adopt a top-down approach and make a post manufacturing environmental audit. In fast-paced new product design process, the longevity of the product and the process development time has been reduced to its lowest level. Therefore, it has become rather difficult to meet the increasing competition of involving change, quicker response to the market as there exists a rapid change in the market economy. Infact the broader design principles suggests to create a product with sole functions and the architecture with no waste or refuse of recycling or refuse of incinerators to go in to grave of the land fill. Therefore, the preliminary design stages are simple and a bottom-up approach for the environmentally-conscience design is insufficient. Thus, the proposal is to make more functionally-oriented set of specific principles to not only directly satisfy the regulations but also provide design valuable attribute. In this context, an integrated top down dynamic & hybrid life cycle analysis-based design approach is presented to address above-mentioned issues with the company policy perspective and to satisfy the regulations and national/international standards in the wake of emerging localization paradigm in manufacturing system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".