Microstructure Analysis of Broached Inconel-718 Gas Turbine Disc Fir-Trees
Bibliographic record
Abstract
Broaching has long been used for machining of fir-trees in gas turbine discs. The fir-tree arrangement is employed for mechanical attachment of blades to disc. Surface and subsurface microstructure changes induced by broaching affect the fatigue resistance of the disc. Therefore, a reliable and quantitative microstructure mapping of the broached fir-trees is essential for the basic optimization of the broaching process. In this study, the microstructure of the surface and subsurface layers of fir-trees in an industrial gas turbine Inconel-718 disc has been analyzed using optical microscopy and scanning electron microscopy. The focus has been on the characterization of defects generated by broaching at the surface and subsurface layers of fir-trees. Also, characterization of the grain size, γ″, γ′, and δ particles from the broached surface to the parent material has been carried out. Characteristics of these microstructural features are key inputs for the development of material based FEM models predicting the fatigue life of the disc. From microstructure studies, the presence of defects such as plucking and distorted layer was observed. The characteristics of these defects (size and morphology) were compared with those of the design limits determined by gas turbine engine manufacturer. Furthermore, significant variation in the volume fraction of δ particles was observed in the area affected by broaching compared with the parent material. These observations are related to the thermomechanical history of the material during the broaching process. Finally, the impact of microstructure evolution on microhardness variation from the broached surface to the parent material is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".