Porosity and cutting forces: from macroscale to microscale machining correlations
Bibliographic record
Abstract
Porous metals, typically produced through powder metallurgy, represent a class of relatively new materials with wide industrial applications, lately extending into the microscale domain. Although produced in near-net shapes, most components fabricated from these materials still require some form of secondary machining. Despite the progress made in the field, relatively little is known either on the inherent cutting mechanism or on the behaviour of these materials under micromachining conditions. The present study reviews the main cutting theories proposed in macroscale machining, along with one of the primary parameters used to describe its machinability performances, namely cutting forces. Then, the feasibility of macroscale concepts is discussed in the context of micromachining technology that is characterized by comparable tool and pore sizes. The microslot cutting experiment performed in a porous titanium sample outlined the relative interplay between the magnitude of the cutting force and porosity of the material. Based on this, it was concluded that the impact of structural porosity on cutting forces experienced during micromachining is significant and therefore further in-depth investigations will be required.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".