Framework for evaluation of the relative contribution of the process on porosity–cutting force dependence in micromilling of titanium foams
Bibliographic record
Abstract
Porous titanium, characterized by interconnected and large open-cell structures, constitutes one of the most promising bone substitutes that are currently available for surgical orthopedic and dental implantation procedures. Since little is known about the behavior of this highly porous material during material removal operations, the main objective of this study was to develop a framework capable of evaluating the effect of cutting speed, cutting depth, and feed rate on the interplay between porosity and cutting force signatures, as experienced during microslot cutting experiments. The comparisons performed between optically determined porosity and cutting force profiles by means of standard random data analysis metrics (correlation coefficient, power spectral density, and coherence) revealed that the presence of a material discontinuity has a prevalent effect on cutting force variation in the case of micromilling processes characterized by (1) less intensive machining regimes and (2) larger cutter/workpiece engagement zones. The proposed methodology is useful in selection of the investigative approach to be taken in assessment of the micromachining-related behavior of highly porous foams subjected to micromilling operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".