Tool replacement based on pattern recognition with LAD
Bibliographic record
Abstract
While traditional maintenance cost optimization is based on finding the reliability, and thus the probability of failure over time, in this paper, we show how to exploit condition monitoring data in machining operation in order to extract intelligent knowledge, and use this knowledge to determine the tool replacement time. This work is motivated by the increasing use of sensors in general, and specifically in condition monitoring. We show how the large volume of data that is now available in many industrial sites can give indications to the machining's operator in order to replace the tool. We use a methodology called Logical Analysis of Data (LA D). This methodology enables us to extract meaningful patterns that describe the state of the tool's wear, based on monitoring and measuring the cutting forces. Unlike the traditional experts' rule-based methods, the extracted patterns are not based on experts' opinion, but on information and hidden relations between the monitored forces. We apply our methodology on data obtained from experiments that are conducted in the laboratory. The experimental data are collected during a turning process of titanium metal matrix composites (TiMMCs). These are new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace, and they are very expensive. In order to validate our methodology, we compare the results obtained when applying LAD to those obtained by using the well-known statistical Proportional Hazards Model (PHM). Findings and conclusion are given in the paper.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".