Effect of thermomechanical processing defects on fatigue and fracture behaviour of forged magnesium
Bibliographic record
Abstract
The microstructural origins of premature fatigue failures were investigated on a variety of forged components manufactured from AZ80 and ZK60 magnesium, both at the test specimen level and the full-scale component level. Both stress and strain-controlled approaches were used to characterize the macroscopically defect-free forged material behaviour as well as with varying levels of defect intensities. The effect of thermomechanical processing defects due to forging of a industrially relevant full-scale component were characterized and quantified using a variety of techniques. The fracture initiation and early crack growth behaviour was deterministically traced back to a combination of various effects having both geometric and microstructural origins, including poor fusion during forging, entrainment of contaminants sub-surface, as well as other inhomogeneities in the thermomechanical processing history. At the test specimen level, the fracture behaviour under both stress and strain controlled uniaxial loading was characterized for forged AZ80 Mg and a structure-property relationship was developed. The fracture surface morphology was quantitatively assessed revealing key features which characterize the presence and severity of intrinsic forging defects. A significant degradation in fatigue performance was observed as a result of forging defects accelerating fracture initiation and early crack growth, up to 6 times reduction in life (relative to the defect free material) under constant amplitude fully reversed fatigue loading. At the full-scale component level, the fatigue and fracture behaviour under combined structural loading was also characterized for a number of ZK60 forged components with varying levels of intrinsic thermomechanical processing defects. A novel in-situ non-contact approach (utilizing Digital-Image Correlation) was used as a screening test to establish the presence of these intrinsic defects and reliably predict their effect on the final fracture behaviour in an accelerated manner compared to conventional methods.
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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 | 0.000 |
| 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".