Probing the Elastic-Plastic, Time-Dependent Stress Response of Test Fasteners Using Finite Element Analysis
Bibliographic record
Abstract
Abstract The evolution of global and local stress/strain conditions in test fasteners under test conditions is investigated using elastic-plastic, time-dependent finite element analyses (FEA). For elastic-plastic response, tensile data from multiple specimens, material heats and test temperatures are integrated into a single, normalized flow curve from which temperature dependency is extracted. A primary creep model is calibrated with specimen- and fastener-based thermal relaxation data generated under a range of times, temperatures, stress levels, and environments. These material inputs are used in analytical simulations of experimental test conditions for several types of fasteners. These fastener models are constructed with automated routines and contact conditions prescribed at all potentially mating surfaces. Thermal or mechanical room temperature preloading, as appropriate for a given fastener, is followed by a temperature ramp and a dwell time at constant temperature. While the amount of thermal stress relaxation is limited for the conditions modeled, local stress states are highly dependent upon geometry (thread root radius, for example), preloading history and thermal expansion differences between the test fastener and test fixture. Benefits of this FE approach over an elastic methodology for stress calculation will be illustrated with correlations of stress corrosion cracking (SCC) initiation time and crack orientations in stress concentrations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".