Environmental Analysis of Sustainable and Traditional Cooling and Lubrication Strategies during Machining Processes
Bibliographic record
Abstract
Due to rising demands of replacing traditional cooling strategies with sustainable cooling strategies, the development of sustainable strategies such as minimum quantity lubrication (MQL) of nano-cutting fluids (NCFs) is on the rise. MQL of NCFs has received a lot of attention due to its positive impact on machining process efficiency. However, environmental and human health impacts of this strategy have not been fully investigated yet. This work aims to investigate the impacts of MQL of molybdenum disulfide (MoS2), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO2), and aluminum oxide (Al2O3) NCFs by employing a cradle-to-gate type of life cycle assessment (LCA). Besides, this paper provides a comparison of the impacts and machining performance when utilizing MQL of NCFs with other cooling strategies such as traditional flood cooling (TFC) of conventional cutting fluids and MQL of vegetable oils. It was found that NCFs have higher impacts than conventional cutting fluids and vegetable oils. The impacts of TiO2-NCF and MoS2-NCF were lower than the impacts of MWCNTs-NCF and Al2O3-NCF. MQL of NCFs presented higher impacts by 3.7% to 35.4% in comparison with the MQL of vegetable oils. TFC of conventional CFs displayed the lowest impact. However, TFC of conventional cutting fluids is contributing to severe health problems for operators. MQL of vegetable oils displayed higher impacts than TCFs of conventional cutting fluids. However, vegetable oils are considered to be environmentally friendly. According to the findings, the MQL of vegetable oils is the most sustainable strategy for machining processes with associated low/medium cutting temperatures. While MQL of TiO2 and MoS2 NCFs are the sustainable strategy for machining processes associated with high cutting temperatures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".