Mejora continua aplicada a las técnicas y procedimientos de la fundición de placas de aluminio en alúmina S.A.
Bibliographic record
Abstract
In this project the mold change process for the preparation of aluminum plates is performed with reference to the new technology implemented by Wagstaff Industries around the world who have implemented technology adjustable cast ingots in countries like Brazil , China, Netherlands, Russia, Australia , South America , Taiwan, United Kingdom, Germany , Croatia , Canada and the U.S., this technology platform for setting iron molds are used and to have 87 different sizes that can be used as variables molds reducing the number of sets of parts to produce a range of sizes of rolled bars or plates . All this in order to develop a high quality in the plates recovered and increased. The plates or ingots produced by this technology has a minimum of curvature at the end surfaces leading to higher and high recovery rates. The high quality of these ingots using this technology generates significant savings and cut operating activities. Given the above and taking into account the issues presented Alumina in terms of increased operating activities due to the use of fixed molds and presents a decrease in recovered, the latter being the indicator by which the process is measured according to the global standard and identifies the loss or gain of the process technology is implemented in the company and thus reflect improvements in the production process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".