Microstructural evaluation and failure analysis of flow forming mandrels: Case studies
Bibliographic record
Abstract
• Metallography reveals defects and structural flaws in cold flow forming mandrels. • Subsurface cracks in failed mandrels identified by SEM, XRD, and stress analysis. • Proper EDM reduces white layer formation and surface tensile stress in mandrels. • Inter-critical annealing improves fracture toughness of DC53 tool steel. This study presents comprehensive metallographic observations on defects and other structural insufficiencies, such as micro-crack development and their causes during cold flow forming of different steel parts. Flow forming is an ideal process for manufacturing parts made of high-strength materials. The process’s high forming forces can produce these parts within tight dimensional and thickness tolerances. This requires the highest standards in surface consistency of hardened flow forming tools to perform serial production of parts (within the range of thousands). This paper shows the importance of structural insufficiencies, particularly under surface area and their role in the failure of flow forming tooling. Tool life is presented for different mandrels made of DC53 cold work steel. All of them were used in the flow forming process. Failure case studies were based on the failed versus well-performing mandrel taken from the production line. SEM, XRD analyses, metallographic studies and residual stresses evaluation were performed. Importance of properly performed EDM to minimize the white (re-cast) layer formation and surface stress is outlined. The present work also points out the role of inter-critical annealing on the fracture toughness of steel DC53.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".