Bibliographic record
Abstract
Accelerated testing in environments aggressive for the specific material have shown that fastener threads that are rolled after strengthening heat treatments have improved resistance to stress corrosion cracking (SCC) initiation. For example, intergranular SCC was produced in one day when machined (cut) threads of high-strength steel (ASTM A193 B-7 and A354 Grade 8) were exposed to an aggressive aqueous environment containing 8 wt % boiling ammonium nitrate and stressed to about 40 % of the steel's yield strength. In similar testing conditions, bolts that were thread rolled before heat treatment (quench and temper) had similar high susceptibility to SCC. However, threads rolled after the strengthening heat treatment exhibited no SCC after a week of exposure, even when stressed to 100 % of the B-7 alloy yield strength. Similarly, intergranular SCC was produced in less than one day when machined (cut) threads of nickel-base alloys (X-750 and aged 625) were exposed to an aggressive 750°F doped steam environment (containing 100 ppm of chlorides, fluorides, sulfates, and nitrates) and stressed to about 80 % of the alloy yield strength. In similar testing conditions, threads rolled after strengthening exhibited no SCC after 50 days of exposure. This beneficial effect of the optimum thread rolling process (i.e., threads rolled after the strengthening heat treatment) is due to the retention of large residual compressive stresses in the thread roots (notches), which mitigate the applied notch tensile stresses resulting from joint design preloads. Use of these material-specific aggressive environments—“chemical cracking” tests—can provide an accelerated test to verify that threads were, in fact, produced by the optimum thread rolling process. The chemical cracking tests could also support fastener acceptance criteria or failure analysis of fasteners with unknown or uncertain manufacturing processes. The achievement of the optimum process effects may not always be detected by more conventional methods (e.g., metallography or hardness testing).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".