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Record W4402487081 · doi:10.1080/00084433.2024.2396639

Investigating the process parameter window for laser powder bed fusion of copper chrome zirconium

2024· article· en· W4402487081 on OpenAlexafffund
M. Trask, D.P. Bishop

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova Scotia
KeywordsZirconiumCopperMaterials scienceFusionProcess windowLaserWindow (computing)MetallurgyInertial confinement fusionProcess (computing)ChromiumOpticsOptoelectronicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Copper-based alloy UNS C18150 has become an attractive material for laser powder bed fusion (LPBF) processing. However, the complexity of LPBF mandates that unique process parameters be devised for each alloy as these are highly sensitive to material chemistry. This work aims to clarify the general boundaries of the process window for the most influential parameters alongside an exploration into the effects of scan strategy on the density. A series of statistical design of experiments were utilised to model density as a response and predict process parameters that would result in minimised residual porosity. Using optimised parameters, specimen with up to 99.1% of theoretical density were produced, and a window of process parameters was established that yielded similarly dense products. Post-build aging of the LPBF parts was also explored. Here, direct aging of the as-built product at 390°C for 100 h resulted in a peak hardness of 83.3 ± 0.6 HRB.

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 score0.361
Threshold uncertainty score0.593

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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
Published2024
Admission routes2
Has abstractyes

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