Machine learning based optimization of a ceramic bushing manufacturing process
Why this work is in the frame
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Bibliographic record
Abstract
Machine learning (ML) has shown great promise in a variety of domains in recent years. ML models are known to require large amounts of labeled training data, keeping small to medium-sized business from utilizing them. This paper presents ML based approach to optimize a ceramic bushing manufac-turing process, by predicting the employed press-fit process as a function of press punch position. Accurate predictions would ensure optimal process configuration, guaranteeing quality and reducing waste. Models are trained in a supervised manner to predict the press-fit process and the ceramic defect probabilities as functions of press punch position. We were able to predict the press-fit process with a mean correlation of 0.996 and assess whether the process would damage the ceramic with a mean precision of 96.7%. Our results exemplify how ML can be used to predict and optimize highly specialised processes even with small datasets.
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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