A novel HGEDM method for evaluating 3-axis CNC machines in green environment under uncertainty
Bibliographic record
Abstract
In the face of digitization in manufacturing industries, the judicious evaluation and selection of cutting-edge CNC machines play a pivotal role in achieving production-grade precision, accuracy and manufacturing agility. The evaluation of 3-axes CNC machines incorporates most sought-after subjective and objective criteria having significant relative weights and green impacts. This research paper presents a novel heterogeneous expert based decision making (HGEDM) framework incorporating a diversified combination of experts having distinct impact factors. The experts’ impact factors so calculated impart significant contributions in computing weighted aggregated performance ratings of the alternatives. To establish the effectiveness of the suggested approach, three practical selection problems are illustrated. The calculated findings are validated with few well-established approaches demonstrating the realistic nature of the suggested methodology. To assess the stability and robustness of the proposed approach, a sensitivity analysis is performed. Besides, Spearman’s rank correlation measure demonstrates that the ranks obtained using the proposed approach are highly close to those derived from several existing methods. Furthermore, both Pearson correlation coefficient and Sample correlation coefficient measures show a strong association between the proposed approach and existing ones. Therefore, the proposed HGEDM approach is considered to be a consistent and effective tool for supporting optimal selection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".