Sustainability Assessment during Machining Ti-6Al-4V with Nano-Additives-Based Minimum Quantity Lubrication
Bibliographic record
Abstract
The implementation of sustainable machining process can be accomplished by different strategies including process optimization and selection of the proper lubrication techniques and cutting conditions. The present study is carried out from the perspective of a sustainability assessment of turning Ti-6Al-4V by employing minimum quantity lubrication (MQL) and MQL-nanofluid with consideration of the surface quality, tool wear, and power consumption. A sustainability assessment algorithm was used to assess the cutting processes of Ti-6Al-4V alloy under a minimum quantity of lubrication–nanofluid to estimate the levels of sustainable design variables. The assessment included the sustainable indicators as well as the machining responses in a single integrated model. The sustainable aspects included in this study were; environmental impact, management of waste, and safety and health issues of operators. The novelty here lies in employing a comprehensive sustainability assessment model to discuss and understand the machining process with MQL-nanofluid, by not only considering the machining quality characteristics, but also taking into account different sustainability indicators. In order to validate the effectiveness of the sustainability results, a comparison between the optimal and predicted responses was conducted and a good agreement was noticed. It should be stated that MQL-nanofluid showed better results compared to the cutting tests conducted under using classical MQL.
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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 | 0.001 | 0.001 |
| 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 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".