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
In this paper, a unique approach for estimating tool life using a hybrid finite element method coupled with empirical wear rate equation is presented. In the proposed approach, the computational time was significantly reduced when compared to nodal movement technique. However, to adopt such an approach, the angle between tool’s rake and flank faces must be constant through the process and at least two cutting experiments need to be performed for empirical model calibration. It is also important to predict the sliding velocity along the tool/flank face interface accurately when using Usui’s model to predict the tool wear rate. Model validations showed that when the sliding velocity was assumed to be equivalent to the cutting speed, poor agreement between the predicted and measured wear rate and tool life was observed, especially at low cutting speed. Furthermore, a new empirical model to predict tool wear rate in the initial or break-in period as a function of Von Mises stress field was developed. Experimental validation shows that the newly developed model substantially improved the initial tool wear rate in terms of trend and magnitude.
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 | 0.000 |
| 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 it