Time-Varying Tool-Chip Contact in the Cutting Mechanics of Shear Localization
Bibliographic record
Abstract
Abstract Shear localization is the dominant chip formation mechanism in machining of high-performance metallic components, such as those made of titanium and nickel-based alloys. This paper presents an analytical thermo-mechanical model considering a new tool-chip contact mechanism due to shear localization. First, it is experimentally shown that the sticking and sliding contact lengths fluctuate with the frequency of shear localization. Second, a cutting mechanics model is developed considering the shear band formation, its rolling on the tool’s rake face, and the time-varying tool-chip contact length with experimental validation. Finally, the transient temperature at the tool-chip interface is predicted by taking the rolling phenomenon and the time-varying heat sources at the tool-chip interface into account. The proposed model shows that at the beginning of every segmentation cycle, the entire tool-chip contact length is dominated by sliding condition with negligible sticking length. When the tool advances, new workpiece material piles up in its front with an increase in the sticking length. Meanwhile, the sliding length decreases due to the drop in the load-bearing capacity of the shear band. When enough material piles up in front of the tool, a new shear band forms, and the entire contact length returns to the sliding condition. This process repeats every time a shear band occurs, causing the cyclic formation of shear bands and time-varying nature of the tool-chip contact length, therefore influencing the temperature and stress evolution at the tool-chip interface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".