Priority order recognition method of module redesign for the CNC machine tool product family to improve green performance
Bibliographic record
Abstract
To solve the sequencing problem of module redesign in the greening process of a CNC machine tool product family, a priority order recognition method based on the fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA) was proposed. A hierarchical model of the functional modules of the CNC machine tool product family was constructed, and the types of functional modules were divided. The generality coefficient of the functional modules was proposed to reflect the influence of the module types on the redesign priority order. A green performance evaluation indicator system for module instances of the CNC machine tool product family was built, based on which a life cycle-oriented green performance priority order recognition method was established. FAHP and GRA were utilized to evaluate the green performance of module instances. Then, the priority order of module redesign can be determined by the ratio of the green performance evaluation value to the generality coefficient. The feasibility and effectiveness of the proposed priority order recognition method were verified by an applied case of module green redesign sequencing of the gantry machine tool product family. The application case showed that the proposed priority-order recognition method based on FAHP and GRA provides a scientific basis for companies to carry out the greening improvement project of the gantry machine tool product family.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".