Titanium Alloy Repair with Wire-Feed Electron Beam Additive Manufacturing Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wire feeding can be combined with different heat sources, for example, arc, laser, and electron beam, to enable additive manufacturing and repair of metallic materials. In the case of titanium alloys, the vacuum operational environment of electron beam systems prevents atmospheric contamination during high-temperature processing and ensures high performance and reliability of additively manufactured or repaired components. In the present work, the feasibility of developing a repair process that emulates refurbishing an “extensively eroded” fan blade leading edge using wire-feed electron beam additive manufacturing technology was examined. The integrity of the Ti6Al4V wall structure deposited on a 3 mm thick Ti6Al4V substrate was verified using X-ray microcomputed tomography with a three-dimensional reconstruction. To understand the geometrical distortion in the substrate, three-dimensional displacement mapping with digital image correlation was undertaken after refurbishment and postdeposition stress relief heat treatment. Other characteristics of the repair were examined by assessing the macro- and microstructure, residual stresses, microhardness, tensile and fatigue properties, and static and dynamic failure mechanisms.
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.
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 | 0.001 |
| 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 it