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Record W4415927253 · doi:10.15353/hi-am.v1i1.6778

Assessing the impact of binder saturation on print quality of binder jetted green samples of regular morphologies

2025· article· W4415927253 on OpenAlexafffund
Alexandra Darroch, E.S. Yang, Mihaela Vlasea

Bibliographic record

VenueProceedings of the Holistic Innovation in Additive Manufacturing (HI-AM) Conference · 2025
Typearticle
Language
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFedDev Ontario
KeywordsSaturation (graph theory)CalipersAlloyProfilometerInkwellManufacturing process

Abstract

fetched live from OpenAlex

A pivotal process parameter in binder jetting additive manufacturing (BJAM) is binder saturation, defined as the volumetric ratio of binder deposited to voids within the powder bed. Improperly tailored binder saturation may lead to printing issues such as binder overspread, increased surface roughness, and layer delamination. These existing issues may be further exacerbated with the use of irregular morphological powders, which have a higher degree of interparticle friction and therefore tend to form powder beds with larger pores. This then slows down binder imbibition into the bed. This research will examine the effect of varying binder saturation on a regular (sphericity of 0.95) powder morphology and the resulting green part qualities using C18150 copper alloy powder. A metric used to assess quality is dimensional fidelity, evaluated using image processing techniques to compare designed vs. actual feature size of key geometric structures such as fine through holes and horizontal slots. Additionally, the green density of prints was evaluated with a precision balance and calipers on cubic samples. It was found that, for regular morphology powders, dimensional error did not scale with decreasing feature size. Thus, uniform compensation factors may be implemented into future CAD designs to improve dimensional accuracy.

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.091
GPT teacher head0.349
Teacher spread0.258 · 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.

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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