Inclusion engineering of steel to prevent chemical tool wear
Bibliographic record
Abstract
Trent introduced the concept of tribological conditions of seizure at the tool–chip interface, when the normal pressure exceeds the flow stress of asperities so that the asperities are squeezed to make atomic contact. In consequence, chemical dissolution of the tool into the chip occurs by a diffusion mechanism, causing chemical wear. Oxley incorporated the concept of seizure in his quantitative model for flow chip morphology. Oxley introduced the concept of equilibrium shear angle in his quantitative model for flow chip morphology, which incorporates the work of shear in the secondary shear zone instead of friction, once atomic contact is established, but Oxley's model ignored potential interaction from metallurgical softening events in the secondary shear zone, which led to prediction of unrealistically high temperature at high cutting speeds. In fact, metallurgical softening events do occur particularly at high cutting speeds, causing shear localisation, which leads to significant deviation from Oxley's model predictions. In this paper, Oxley's model will be extended to capture the interaction of shear localisation in the secondary shear zone on the mechanics of metal cutting. Dynamic recrystallisation, phase transformation of the matrix and geometric softening owing to second phase particles are identified as important microstructural softening events causing shear localisation, which could intervene before the equilibrium shear angle is reached. The occurrence of shear localisation is shown to alter the mechanics of metal cutting, chip morphology and the tool wear mechanism. A phenomenological database in model alloys will be presented to validate the model assumptions. The application of the model in the design of self-lubricating free cutting steel for moderate and high cutting speeds will be examined.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".