Prediction of Component Failure using ‘Progressive Damage and Failure Model’ and Its Application in Automotive Wheel Design
Bibliographic record
Abstract
Damages (fracture) in metals are caused by material degradation due to crack initiation and growth due to fatigue or dynamic loadings. The accurate and realistic modeling of an inelastic behavior of metals is essential for the solution of various problems occurring in engineering fields. Currently, various theories and failure models are available to predict the damage initiation and the growth in metals. In this paper, the failure of aluminum alloy is studied using progressive damage and failure material model using Abaqus explicit solver. This material model has the capability to predict the damage initiation due to the ductile and shear failure. After damage initiation, the material stiffness is degraded progressively according to the specified damage evolution response. The progressive damage models allow a smooth degradation of the material stiffness, in both quasi-static and dynamic situations. Further in this paper, the material model is validated for tensile and shear failure using standard specimens. Then the validated material model is used in the damage prediction of the aluminum wheel due to compression load and good correlation is achieved within 5% of deviation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".