A Comparative Thermo-Mechanical Reverse-Flow-Forming Analysis on High Strength Alloys: A DOE-Based Numerical Study
Bibliographic record
Abstract
Flow-forming is a highly precise metal forming process extensively used in aerospace and defence sectors for manufacturing high strength, thin-walled tubular components.In the presented research, a detailed finite element (FE) thermo-mechanical model using Abaqus/Explicit is developed to investigate the influence of key process parameters on the structural integrity of the flow-formed tubes.This study evaluates three different types of alloys, Maraging Steel -250 (MDN 250), 15CDV6, and AISI 4130.These materials are selected due to their widespread use in flow-forming applications pertaining to aerospace and defence sectors.The use of flow-forming on these materials enhance mechanical properties through work hardening, ensures precise dimensional control, and reduces material wastage, making them optimal choices for thin-walled tubular components in critical applications.A Taguchi L9 orthogonal array is utilized to systematically analyse the influence of process parameter in feed ratio, percentage reduction, and axial stagger, and their effects are analysed on critical dimensional outcomes such as ovality, diametral growth, and spring-back.Additionally, thermo-structural response outputs like equivalent plastic strain (PEEQ) and temperature distribution at the roller-preform interface are monitored to evaluate the severity of these effects across the three materials.The results indicate a strong correlation between input parameters and PEEQ, with higher strain levels leading to an increased risk of defects and potential onset of failures.Significant thermal gradients are observed as the rollers engage with the preform, with peak temperatures localized at the contact zone due to frictional heating.This localized temperature rise contributes to material softening, thereby, promoting relevant plastic flow and an improved surface conformity.These insights provide a valuable basis for understanding materialprocess interactions and optimizing flow-forming conditions to enhance dimensional precision with minimal defects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".