Mechanical and Thermal Characterisation of Millscale Modified Al-Cu Alloy for Artificial Intelligence Systems
Bibliographic record
Abstract
Continuous research into critical functional property enhancement of materials employed in artificial intelligence systems is imperative to overcome performance limitations. This study investigated the thermal and mechanical properties of stir-cast fabricated Al-Cu alloy modified with addition of iron-millscale (IMS) particles varied from 2-6 wt.%. The alloys microstructure was analysed using both optical and scanning electron microscope coupled with energy dispersive spectroscopy (SEM/EDS). PerkinElmer Thermogravimetry/Derivative thermal analyser was used to assess the alloys thermal characteristics while the mechanical properties were evaluated using relevant state of the art equipment. Results show that the best thermal and mechanical properties comparable to established standards were achieved at 6 wt.% IMS particle addition. Contributions to the alloy enhanced performances stemmed from the structure refining propensity of IMS particles. Based on the thermal and mechanical properties demonstrated, the alloy is recommended for application in pneumatic offshore valve actuator used in oil and gas flow process lines.
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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 | 0.000 | 0.000 |
| 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".