Electron beam weld modelling for near-β Ti-alloy
Bibliographic record
Abstract
The longer service life and lighter weight are two main driven forces and challenges for aerospace industries to explore more designs, materials and manufacturing technologies. As a representative of light-weight and high strength materials, Ti-alloy plays an important role in critical load-carrying components in air crafts. Compared to traditional α+β alloy, near-β Ti-alloy has two key advantages, higher yield strength and better corrosion resistance and thereby becomes increasingly prevalent. Moreover, electron beam welding (EBW), as an advanced joining technology with high energy density beam can achieve material joining by one pass, which has marked superiorities on efficiency and distortion control compared with traditional multi-pass welding. The combination between above material and manufacturing provides us a great opportunity to improve the performance of structural components with lower costs. In this project, EBW process and material modelling is conducted to achieve following aims. 1）The prediction of residual stress and distortion, as two typical negative effects induced by welding. Although there is some research have been done to investigate the weldability of near-β Ti-alloy, there is no any published residual stresses characterization for near-β Ti-alloy. 2) The micro-constituent prediction for optimization of heat treatment. Yield strength of near-β Ti-alloy would decrease markedly due to α precipitate being dissolved during welding. The subsequent post-weld heat treatment is vital to regulate material properties. Particularly, significant variation of properties also causes a great challenge to residual stress prediction in turn. The following two experiments are conducted to support the modelling work. Dilatometry testing are used to characterize solid state phase transformation (SSPT) behavior and volumetric effects induced by SSPT. Moreover, a yield strength model based on temperature history are constructed by a series of tensile testing at different temperatures. This work hopes to reveal some special properties of near-β Ti alloy in terms of weld metallurgy and what need to be careful and satisfied in modelling works for accurate prediction of residual stress, distortion and microstructures, which could promote the better welding application of near-β Ti-alloy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".