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Record W4413739009 · doi:10.1016/j.aime.2025.100170

Comprehensive pore defect analysis for electron beam-powder bed fusion of Ti48Al2Cr2Nb

2025· article· en· W4413739009 on OpenAlexafffund
Tomisin Ayeni, Paria Karimi, Mohsen K. Keshavarz, Esmaeil Sadeghi, Mahdi Habibnejad-Korayem, Mihaela Vlasea

Bibliographic record

VenueAdvances in Industrial and Manufacturing Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
FundersFedDev Ontario
KeywordsFusionCathode rayMaterials scienceElectronNuclear engineeringNuclear physicsPhysicsEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The process-microstructure relationship of gamma-titanium aluminide (Ti-48Al-2Cr-2Nb) parts fabricated via electron beam-powder bed fusion (PBF-EB) process was investigated. A set of 107 records of process parameter combinations were deployed to analyze and classify relative density, lack-of-fusion (LoF) and Gas porosity defects. The bulk density ranges spanned 88 %–99.99 %, with Gas porosity spanning 0.01 %–0.30 %, and lack-of-fusion defects spanning 0.001 %–12 %. Based on literature pertaining to defects addressable via hot isostatic pressing, four classes of density performance were identified: excellent (>99.8 %), good (99.6–99.8 %), poor (98.0–99.6 %), and failed (<98.0 %). For the purpose of this study, a high density outcome (excellent class) is identified to be ideal in terms of expected mechanical properties, specifically strength, fatigue, and ductility. Pore properties were mapped against different energy representations, for example, volumetric energy density (VED) and normalized enthalpy (NE). The results showed the importance of utilizing NE to visualize data and identify zones in the process parameter space where best performance is expected. A region of NE > 15 is expected to have an excellent performance, whereas NE < 10 is expected to result in unacceptable porosity outcomes.

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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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