Development of an advanced aluminum thermal analysis system for the characterization of the 319 aluminum alloy solidification process.
Bibliographic record
Abstract
Thermal analysis is the study of heat evolution as a molten alloy transforms to a solid. Studying the evolution of heat results in a temperature, time relationship in the form of a cooling curve. Specific characteristics of the 319-aluminum cooling curve are described and related to specific metallurgical properties. Variations in the cooling curves represent changes within the solidification of the alloy and are correlated to changes in the metallurgical properties. In the present work, an aluminum thermal analysis system (AITAS) is constructed to withstand the rigors of day to day foundry conditions. Complete automation has been introduced to reduce the number of variables introduced by the operator to the system. This includes automatic analysis and storage of the results in a database, which allows for later statistical evaluation. Methods of noise filtering and automatic temperature detection have all been improved over previous systems in order to reduce measurement error. Experimental work concluded that AITAS is capable of determining the degree of silicon modification as a result of strontium additions. In addition, AITAS is able to determine which aluminum-copper phases are present including the area fraction of each in the aluminum 319 alloy.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .S76. Source: Masters Abstracts International, Volume: 44-03, page: 1480. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.004 |
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".