A Case Based Reasoning aluminium thermal analysis platform for the prediction of W319 Al cast component characteristics
Bibliographic record
Abstract
Purpose: This paper presents the research on the development of the Aluminum Thermal Analysis Technology Platform (AlTAP) utilizing a Case Based Reasoning (CBR) Caspian shell for interpretation of industrial cooling curves and predicting alloy and cast component characteristics. Design/methodology/approach: CBR being a branch of Artificial Intelligence (AI) that solves problems based on understanding and adaptation of previous experiences is suitable for interpretation of the AlTAP results since this is a knowledge intensive activity which requires a fair amount of experience. Findings: The integrated AlTAP and CBR system was found to be useful for the prediction of melt thermal characteristics, cast component mechanical and structural properties. Practical implications: Industrial trials confirmed the technical capabilities of the AlTAP/CBR Platform for the on-line quality control and prediction of 319 melt characteristics and the aluminum engine block’s (Cosworth casting process) engineering specifications. Originality/value: An automated AlTAP Platform integrated with a CBR system is a new Quality Control concept in the area of the aluminum automotive casting.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".