Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it