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Record W7112918134

Electron beam weld modelling for near-β Ti-alloy

2025· article· en· W7112918134 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2025
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsWeldabilityResidual stressWeldingAerospaceService lifeMaterial propertiesCorrosionElectron beam weldingDistortion (music)
DOInot available

Abstract

fetched live from OpenAlex

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.<br/><br/>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.<br/><br/>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.<br/><br/>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.<br/>

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.289
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it