Bibliographic record
Abstract
A new computer model to predict fatigue life based on the evolution of damage in a structure is presented.The experimental data used to validate the model is provided by the SAE Fatigue Design and Failure Committee (FDE).They initiated the total life project with the objective to improve fatigue life prediction and they have been working on the experimental data for around 8 years.Damage in a fatigue test is caused by changes at the microstructural level such as microcracks and pores that usually cannot be observed.Damage scales the elasticity tensor(D).For an isotropic scalar damage field 0 ≤ d(x, t) ≤ 1.0.If d(x, t) = 0 then there is no damage and the elasticity tensor D is unchanged.When damage at a point x increases to d(x, t) = 1.0, then the elasticity tensor D is the zero tensor and the point in the structure is considered to be cracked.The model assumes that damage evolution can be computed as a function of the dissipation of hysteresis loop for a sequence of Ramberg-Osgood equations for a sequence of fatigue load cycles.This model does not use the Paris-Erdogan equation for crack growth.Two model parameters are the coefficients of the Ramberg-Osgood equation.The third model parameter is the total dissipation rate for the damage variable to reach a value of 1.0.Computer iii simulations of fatigue tests with block loading are demonstrated.A high resolution plane strain FEM analysis that resolves the strain field near a stress concentration is shown to be necessary to achieve accurate predictions of fatigue crack nucleation and fatigue crack growth.The predicted fatigue crack nucleation and crack growth rates are in close agreement with the SAE experimental data.I would like to express my sincere appreciation to my supervisor, Professor John A. Goldak, for the continuous support throughout the journey of this work, for his patience, motivation, enthusiasm, guidance and immense knowledge.I have learned many things since I became Dr. Goldak's student.This thesis would not be accomplished without his constant involvement and contribution into my research.His friendly guidance and expert advice have been invaluable throughout all stages of this work.I could not have imagined having a better advisor and mentor for my masters study.No words exist to express the role he played in my life.I wish to express my gratitude to Goldak Technologies Inc, especially to Mr. Stanislav Tchernov and Mr. Jianguo Zhou for developing the code and provide support with the software.I would like to thank my colleague Hossein Nimrouzi for generating the FEM Mesh and his guidance with VrSuite analysis.I would also like to
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".