Prediction of energy consumption and environmental implications for turning operation using finite element analysis
Bibliographic record
Abstract
This article is concerned with the experimental and numerical investigation of energy consumption involved in the turning of Ti6Al4V titanium alloys. Energy consumption of a machining process is considered as an important machining performance indicator. This article aims to propose an approach for the prediction of energy consumption and related environmental implications using finite element modeling simulations. Machining experiments were conducted using uncoated carbide tools under dry cutting environment. DEFORM-3D software package was utilized to simulate finite element–based machining simulations. Experimental validation was mainly conducted by focusing on the cutting forces and power consumption measurements. Simulated results of the cutting force and power consumption were found in a good agreement with the experimental findings. The amount of CO 2 emission resulting from energy consumption during the machining phase is highly dependent on the geographical location. This study also incorporated the energy mix of United Arab Emirates for the environmental calculations. Finally, in the light of proposed methodology, possible future directions and recommendations have also been presented.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".