Differential Scanning Calorimetry Fingerprints of Various Heat-Treatment Tempers of Different Aluminum Alloys
Bibliographic record
Abstract
Heat-treatable cast and wrought aluminum alloys are widely used for structural applications in the automobile and aerospace industries. To assess and diagnose the production and quality problems related to industrial heat treatments, differential scanning calorimetry (DSC) was used as a tool in the present work to determine the thermal histories of samples that had undergone different tempers of three commonly used aluminum alloys, namely a high-pressure die-cast AlSi10Mg0.3Mn alloy, permanent-mold cast Al-Si-Cu 319 alloy, and extruded Al-Mg-Si AA6082 alloy. Various peaks detected in the DSC curves were analyzed and characterized to identify the precipitation/dissolution reactions of metastable phases, aiming to establish a “fingerprint” of each temper of the three experimental alloys. Results showed that both the number and size of exothermic peaks varied with the temper owing to distinct precipitation behaviors, providing an effective means of fingerprinting the various tempers. Meanwhile, electrical conductivity and microhardness data provided the supplementary support for the fingerprinting. The thermal histories of three experimentally heat-treated alloys were well traced and distinguished by the combination of DSC characteristics and electrical conductivity and microhardness results, promoting the DSC application in the quality control and verification of industrial heat treatments. In addition, the microstructures after the various tempers were observed to confirm the evolution of the precipitation reactions revealed in the DSC curves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".