Big Data Accumulation of L-Shape Extruded Alloys for Interior Parts for High-Speed Trains
Bibliographic record
Abstract
L-Shape extruded alloys were manufactured by adopting aluminum alloy as the candidate lightweight alloy to be used for interior and exterior materials of high-speed trains. The cast product was extruded using the air slip (AS) casting method and the direct casting (DC) method. The product was again heat-treated with T5 or T6 tempering. According to literature research, the candidate alloys were selected as 6063, 6N01, 6061, 6060, 6005 and 5083 alloys. These alloys were extruded after casting and heat-treated and their properties such as the hardness, microstructure and tensile properties were evaluated. The hardness, microstructure and tensile properties of the selected 6063, 6N01, 6061, 6005 and 5083 aluminum alloys in the present study are similar to those of external materials made by Alcan, Canada. Mechanical properties of the extruded materials were comparable to those of external materials (manufactured by Canada, Alcan). The hardness, microstructure, and extrusion characteristics of AA6063, AA6N01, AA6061, AA6005, AA6060 and AA5083 alloys selected in the present study through literature review are similar to those of external materials (Canada, Alcan). By performing extrusion, under the conditions of high-speed railway, the process conditions for manufacturing extruded materials with complicated shape to meet the requirements of vibration resistance and airtightness have been established. Therefore, it was proved to be sufficient as the interior and exterior materials of high-speed train.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".