Characterization of machining-induced residual stresses in titanium-based alloys
Bibliographic record
Abstract
Machining-induced residual stresses (RS) have a major impact on the fatigue life of critical aero-engine parts subjected to dynamic loads in harsh environments.Their state and magnitude can be controlled by careful selection of cutting conditions.Tensile RS are extremely harmful as they accelerate crack nucleation and propagation, diminishing the resistance to fatigue failures.It is crucial to identify cutting parameters that promote desirable compressive RS in critical parts without compromising other aspects of surface integrity. Limited information is available in the open literature on machining-induced RS in Ti-alloys.In this research, an extensive experimental investigation is performed of the effect of cutting parameters on RS in two aerospace grade Ti-alloys, Ti-64 and Ti-6246, used for aeroengine fan and compressor parts.Cutting is performed at conditions relevant to industry.This is coupled with a comprehensive evaluation of surface integrity including RS, surface roughness, the near-surface microstructure, and hardness distribution.Based on x-ray diffraction measurements, empirical models are developed that offer fast and accurate predictions of surface RS.For the investigated finish turning regime, RS are compressive in nature.Due to a conflict between RS and surface finish, guidelines are established for the optimal selection of cutting parameters.Empirical models are non-generic, cannot be extrapolated, and cannot offer a physical interpretation of the phenomena that govern the cutting process.Available FE models for Tialloys mostly focus on chip formation and force prediction, and rarely extend to RS.In this work, a 2D FE model is constructed using DEFORM TM for the prediction of machininginduced RS in Ti-alloys, and is optimized for accuracy and computational efficiency.This is preceded by a numerical study on the relative contribution of thermal loads, mechanical loads, and phase transformations to the resultant stress state in commercially available materials.The FE model is firstly validated against experimental machining forces, cutting temperatures, and RS.It is then used as a virtual machining medium to gain insight into the effect of cutting parameters, tool edge preparation, flank wear, and chip segmentation on residual stress formation.FE predictions for Ti-6246 show that RS are highly sensitive to flank wear, which can cause a severe shift from the compressive to the tensile state.For the investigated cutting regime, residual stress prediction errors for Ti-64 are limited to ±10% provided that chip segmentation is modeled at relatively high cutting speeds.
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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 | 0.000 |
| 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 | 0.000 | 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".