Bibliographic record
Abstract
The increasing cost of energy, growing global competition, and increasing customer demand for cheaper and more efficient products has placed tremendous pressure on the manufacturing sector to dramatically improve machining efficiency. While improving the efficiency of machining processes increases the competitiveness and profitability of the manufacturing facility, it also results in a cleaner environment and more sustainable processes in terms of better utilization of resources, reduction of waste, efficient use of energy, and lesser CO2 emission. In manufacturing the concept of sustainability is well defined and implemented on the system level, but this is not the case on the micro-level when it comes to machining processes. With this in mind, this paper analyzes the concept of hybrid machining as a possible means of enhancing machining process sustainability by reducing power consumption, lead, and setup times. Two case studies are presented: turn-grind and mill-grind to illustrate the concept. The collected machining data have been used to correlate the energy consumption, CO2 emission, and cycle time for the two approaches used. The results from the presented case studies are promising as they show the benefits of the hybrid approach on energy consumption, CO2 emission, and cycle time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".