Modeling of Residual Stress Fields and Their Effects on Fatigue Crack Growth
Bibliographic record
Abstract
Fatigue Crack Growth (FCG) is a deleterious physical phenomenon in engineering materials, which is intensified by the presence of tensile Residual Stress Fields (RSF), while compressive RSF has been shown to delay the FCG phenomenon.However, several challenges make it difficult to fully incorporate the beneficial effects of compressive RSF into the design process in aerospace and other engineering industries.As such, this study is designed to understand and quantify the effects of RSF on the FCG phenomenon in thick aluminum alloy specimens.Experimental studies were conducted on specimens made of 7050-T7451 aluminum alloy in order to obtain the material properties required for a FCG model.In addition, FCG tests on Single-Edge Notched Tension (SENT) specimens with well-defined RSF were conducted for the verification of the FCG model.Finite Element Analysis (FEA) software (ABAQUS™) was used to simulate the FCG in RSF, and to analyze the redistribution of RSF due to FCG.ABAQUS was used first to introduce a RSF through a well-controlled four-point bending simulation, which was set as an initial condition to the FCG simulation.Several FCG test simulations were conducted to evaluate the crack closure and plastic wake effects on FCG.As part of these simulations, three test cases were considered: a large stress ratio (R = 0.7), a low stress ratio (R = 0.05) and a negative stress ratio (R = -1).For test case 1 (R = 0.7), the calculation of the FCG rate shows no indication of crack growth retardation due to the presence of the compressive RSF.However, the FCG rate was retarded in the test cases with low and negative stress ratios (R = 0.05 and -1).The
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".