The Effects of Nonproportional Biaxial Loading Paths on Ductile Fracture Initiation: A Void Growth Analysis
Bibliographic record
Abstract
Abstract The effects of nonproportional biaxial loading paths on ductile fracture initiation toughness are studied in this article. To this end, the growth of a cylindrical void (hole) located in front of a mode I plane strain crack has been studied using large-deformation finite element analysis. A specific microstructural feature of a steel alloy was thoroughly studied by having a single void positioned at a fixed distance from the crack tip and void that was equal to 10 times the diameter of the void. In particular, the nonproportional biaxial loading path effects on the crack tip blunting, void growth, ligament reduction, and near-tip stress fields are investigated computationally. Under small-scale yielding conditions, one proportional loading and two nonproportional loading paths are applied to the modified boundary layer models, covering both low-constraint (negative T-stress) and high-constraint (positive T-stress) crack tip conditions. We have observed that the nonproportional load paths have a marked effect on the void growth, crack tip blunting, and the resulting ligament reduction. By applying the simple criteria for the coalescence of the crack tip and void, the ductile fracture initiation toughness is estimated. It is shown that the ductile fracture toughness is dependent on loading paths and constraint conditions (the T-stress ratios). Compared with the proportional biaxial loading case, different load sequences of the biaxial loads will result in either an increase or a decrease the fracture initiation toughness. This effect is particularly significant for specimen geometry with low-constraint conditions (i.e., negative T-stress ratios). Results from this study are of relevance to ductile fracture assessment of components or structures, such as pressure vessels that operate under nonproportional biaxial loading conditions.
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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".