Physics-Based Prognostics for LCF Crack Nucleation Life of IMI 685 Aero-engine Compressor Disc
Bibliographic record
Abstract
A life cycle management-expert system (LCM-ES) framework is employed in this work for physics-based prognostics of a compressor disc. The modeling approach involves the integration of both global behavior and localized response of component at the microstructural level. This paper presents the results of a low cycle fatigue (LCF) case study for a near alpha titanium alloy (IMI 685) high pressure compressor disc using a microstructure based damage model and finite element analysis results. Both deterministic and probabilistic crack nucleation lives are determined at the two critical locations. The lognormal distributions of α-grain structure of IMI685 and hard alpha (HA) inclusions is considered in the probabilistic analysis, while the deterministic life is predicted based on their extreme values that would represent the worst life. In the LCF modeling, the plastic strain estimation assumes an empirical coefficient that has a strong dependence on the alpha grains and defect size. The proposed life prediction model is capable of capturing the effect of the grain size and hard alpha particle density variation on the LCF crack nucleation life. The worst case deterministic life corresponds well with 0.1% probability of failure and lie around 3542 and 4710 cycles respectively for the primary fracture critical location in the disc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".