Cutting tool remaining useful life during turning of metal matrix composites
Bibliographic record
Abstract
In this paper, the conditional reliability function and the Remaining Useful Life (RUL) of a cutting tool are estimated as a function of the current condition's states. RUL is estimated based on the available information obtained from condition monitoring. The cutting forces' measurements define the states, and are considered as the monitoring signals that offer diagnosis of the tool wear state. The cutting tool is used under constant machining parameters, namely the cutting speed, the feed rate, and the depth of cut. Experimental data is collected during turning titanium metal matrix composites (TiMMCs) which are a new generation of materials and have proven to be viable in aerospace application. Two modeling tools are used to model the tool's reliability and hazard functions; The Proportional Hazards Model (PHM), which is a statistical tool that uses EXAKT software, and the Logical Analysis of Data (LAD), which is a machine learning tool that uses cbmLAD software. A comparison between the two approaches is given. The results are presented, and the practical use of these results is discussed. The Remaining Useful Life (RUL) of a cutting tool during turning TiMMCs, and its conditional reliability function are estimated as functions of the current condition's states.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".