An Innovative Approach to Improving Residual Stress Distribution and Metallurgical Refinement of Forged AA7175 Applied in the Aeronautical Industry
Bibliographic record
Abstract
In this study, a novel approach for residual stress (RS) distribution on forged AA7175 is considered to replace and simplify the manufacturing process, based on the lean manufacturing concept. AA7175 alloy is a quench-sensitive material applied in the aeronautics industry, which is subjected to vibration and cyclic loads leading to fatigue failure. Generally, costly postprocessing operations, such as shot peening, are used to modify RS on the surfaces of parts. Considering the fact that this operation is usually performed manually and is costly, the industrial sectors have been searching for an alternative to simplify the process. Here, quenching and T74 aging are found to advantageously modify RS distribution by forming compressive RS on parts’ surface layers. The proposed heat treatment allows for the removal of the shot-peening process, helping to reduce the costs associated with the manufacturing process and to increase production quality.
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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".