Role of energy consumption, cutting tool and workpiece materials towards environmentally conscious machining: A comprehensive review
Bibliographic record
Abstract
Metal-cutting process deals with the removal of material using the shearing operation with the help of hard cutting tools. Machining operations are famous in the manufacturing sector due to their capability to manufacture tight tolerances and high dimensional accuracy while simultaneously maintaining the cost-effectiveness for higher production levels. As metal-cutting processes consume a great amount of input resources and generate some material-based waste streams, these processes are highly criticized due to their high and negative environmental impacts. Researchers in the metal-cutting sector are currently exploring and benchmarking different activities and best practices to make the cutting operation environment friendly in nature. These eco-friendly practices mainly cover the wide range of activities directly or indirectly associated with the metal-cutting operation. Most of the literature for sustainable metal-cutting activities revolves around the sustainable lubrication techniques to minimize the negative influence of cutting fluids on the environment. However, there is a need to enlarge the assessment domain for the metal-cutting process and other directly and indirectly associated practices such as enhancing sustainability through innovative methods for workpiece and cutting tool materials, and approaches to optimize energy consumption should also be explored. The aim of this article is to explore the role of energy consumption and the influence of workpiece and tool materials towards the sustainability of machining process. The article concludes that sustainability of the machining process can be improved by incorporating different innovative approaches related to the energy and tool–workpiece material consumptions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".