Development of a knowledge-based system for the control of 319-aluminum melt quality and prediction of casting characteristics.
Bibliographic record
Abstract
Knowledge Based Systems are computer programs used in situations where the use of heuristic knowledge and reasoning offers the only possibility of a solution mainly because the problem being studied is too complex to be represented in an algorithmic form. In general terms, such systems address and solve knowledge-intensive problems that can have many acceptable answers. The interpretation of Thermal Analysis results is a suitable task for artificial intelligence technology since it is a knowledge intensive activity in which several factors interact and modify the characteristics of the cooling curves. In the present work, an Aluminum Thermal Analysis System (AlTAS) developed at the University of Windsor was implemented at a casting plant and after verification of its performance in an industrial scenario a Case-Based Reasoning System coupled with a Statistical Process Control module were developed to upgrade the capabilities of the AlTAS. The system will be used to control, on-line and prior to casting, the quality of the W319-Al melt used to produce aluminum engine blocks under the Cosworth casting process, and predict their tensile properties and microstructure.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .P45. Source: Masters Abstracts International, Volume: 40-03, page: 0777. Advisers: Reza Lashkari; Jerry Sokolowski. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".