IMPROVING THE FORMABILITY OF STAINLESS STEEL 321 THROUGH MULTISTEP DEFORMATION FOR HYDROFORMING APPLICATIONS
Bibliographic record
Abstract
Tube hydroforming (THF) is a well established process in the automotive industry and its application is being extended to the aerospace for manufacturing complex geometries. However, most of the alloys used in aerospace are high in strength and low in formability, which renders the application of THF more challenging. The objective of this paper is to present a method to increase the formability of an austenitic stainless steel. A multistep forming process was simulated through interrupted uniaxial tensile testing experiments to study the influence of the latter process on formability. The tensile test was divided into several deformation steps with a stress relief heat treatment after each forming step. The results indicated that the application of intermediate heat treatments considerably increased the formability of the stainless steel 321 alloy (SS321). Microstructure evolution as a function of deformation or heat treatment parameters was also investigated and revealed the formation of strain-induced martensite after the first deformation and heat treatment cycle without any deleterious effect on formability enhancement.
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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".