MétaCan
Menu
Back to cohort
Record W2810135914 · doi:10.1007/s11661-018-4767-0

Effect of Process Parameters on the Crack Formation in Laser Metal Powder Deposition of Alloy 718

2018· article· en· W2810135914 on OpenAlexaff
Andreas Segerstark, Joel Andersson, Lars-Erik Svensson, Olanrewaju Ojo

Bibliographic record

VenueMetallurgical and Materials Transactions A · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Manitoba
FundersGKN Aerospace ServicesTillväxtverket
KeywordsLiquationCrackingMaterials scienceAlloyDeposition (geology)MetallurgyLaser power scalingMetal powderLaserSubstrate (aquarium)SuperalloyMetalComposite materialOptics

Abstract

fetched live from OpenAlex

Abstract Cracking in Alloy 718 using laser metal powder deposition has been evaluated in this study. It is found that the material is susceptible to cracking when the laser power is high, the scanning speed is high, and the powder-feeding rate is low. Almost all the cracks are located close to the center of the deposited wall and propagate in the normal direction to the substrate. Evidence of liquation are found at the cracked surfaces, and since all cracks reside in regions which are reheated several times, the cracks are determined to most likely be heat-affected zone liquation cracks. The influence of respective process parameter was evaluated using a design of experiment approach. It is shown that, when the powder-feeding rate is incorporated as a variable, the heat input is not a suitable indicator for the hot cracking susceptibility in laser metal powder deposition of Alloy 718. A combinatory model using the power density together with the heat input is therefore proposed.

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 score0.023
Threshold uncertainty score0.627

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)0.0010.000

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMetallurgical and Materials Transactions ASame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207