Numerical Modeling of the Failure of Magnesium Tubes Under Compressive Loading
Bibliographic record
Abstract
A new finite element (FE) specific failure criterion utilizing hardening rates to quantify bending stress is implemented into the MAT_124 material model in the commercial software LS-DYNA to simulate fracture of extruded AZ31 and cast AM60 magnesium alloy tubes. The simulations are performed by requiring element erosion of hexahedral solid elements in a three-dimensional (3D) FE model when the failure criterion is satisfied at any point in the simulation. Experimental stress–strain curves from tensile and compression tests of the materials are used as inputs in the model. The simulations reproduce the measured load displacement data as well as general features of the experimental failure modes of round and rectangular tubes undergoing axial crush tests. The model is applied to investigate the effects of a variety of design features, such as varying tube wall thickness, preformed bulges, alternate bands of Al and Mg alloys, and cladding Al on magnesium, on the macroscopic strain to failure. The results show that adding multiple preformed bulges to the tubes can increase the strain to failure and reduce the force required to cause deformation. Adding a single bulge concentrates the strain causing reduced macroscopic strain to failure. Placing sections of reduced wall thickness or brazing in sections of aluminum causes stress concentrations which reduce the macroscopic strain to failure. Cladding aluminum onto the outside of the magnesium tube is shown to improve strain to failure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".