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The Development of Weldable Nickel-Based Superalloys and Technologies for Repair and Additive Manufacturing of Turbine Engine Components

2024· article· en· W4403303489 on OpenAlexfundno aff
Alexandre Gontcharov, Paul Lowden

Bibliographic record

VenueAdvances in materials technology for fossil power plants : · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsnot available
FundersMcGill University
KeywordsSuperalloyTurbineNickelManufacturing engineeringMaterials scienceMetallurgyAutomotive engineeringMechanical engineeringEngineeringMicrostructure

Abstract

fetched live from OpenAlex

Abstract High gamma prime Ni-based superalloys comprising ≥3.5 % Al are difficult to weld due to high propensity of these materials to weld solidification, heat affected zone liquation, and stress-strain cracking. In this study the root cause analysis of cracking and overview on the developed weldable Ni-based superalloys for repair of turbine engine components manufactured from equiaxed (EA), directionally solidified (DS), and single crystal (SX) materials as well as for 3D AM is provided. It is shown that the problem with the solidification and HAZ liquation cracking of turbine engine components manufactured from EA and DS superalloys was successfully resolved by modification of welding materials with boron and silicon to provide a sufficient amount of eutectic at terminal solidification to promote self-healing of liquation cracks along the weld - base material interface. For crack repair of turbine engine components and 3D AM ductile LW4280, LW7901 and LCT materials were developed. It is shown that LW7901 and LCT welding materials comprising 30 - 32 wt.% Co produced sound welds by GTAW-MA on various SX and DS materials. Welds demonstrated high ductility, desirable combination of strength and oxidation properties for tip repair of turbine blades. Examples of tip repair of turbine blades are provided.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.614

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 head0.007
GPT teacher head0.230
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

Citations1
Published2024
Admission routes1
Has abstractyes

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