Investigation of sustainability in machining processes: exergy analysis of turning operations
Why this work is in the frame
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Bibliographic record
Abstract
Optimisation of the machining process in terms of minimum cost and minimum energy consumption has already been presented in the literature. This paper is aimed at developing a new methodology for optimising the process to improve the machining sustainability aspects. The exergy analysis method is employed for investigation of sustainability in the dry turning process. Evaluations of exergy efficiency and exergy loss during the process along with the effects of various cutting parameters are performed. The objective of process optimisation is to minimise exergy loss. The optimisation process results in a tool life equation that satisfies the minimum exergy loss requirement. The exergy analysis takes into account the concept of quality as well as the energy footprint to measure the effects of the process on the environment. Comparison of the results of the presented analysis with results from the minimum energy consumption method shows that the developed model can provide the cutting conditions for a more environmentally friendly machining process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it