A Two-Dimensional Transient Thermal Model for Coated Cutting Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prediction of temperature in the tool, chip, and workpiece surface is important to study tool wear, residual stresses in the machined part, and to design cutting tool substrates and coating. This paper presents a finite difference method-based prediction of temperature distribution in the tool, chip, and workpiece surface for transient conditions. The model allows inclusion of anisotropic materials such as coating or different material properties. The energy is created in the primary shear zone where the metal is sheared, the secondary deformation zone where the chip moves on the tool rake face with friction, and the tertiary zone where the flank face of the tool rubs against the finished part surface. The model allows both sticking and sliding friction contact of the moving chip on the rake face of the tool. The distribution of temperature is evaluated by meshing chip, workpiece surface zone, and tool into small discrete elements. The heat transfer among the elements is modeled, and the temperature is predicted at the center of each element. The heat transfer to the tool, workpiece, and chip is iteratively evaluated. The predicted temperature values are compared against the experimental measurements collected with coated tools in turning.
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.
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 | 0.000 | 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 | 0.001 |
| 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 it