Microstructural evaluation and failure analysis of flow forming mandrels: Case studies
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.000 | 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 it