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Record W4415927714 · doi:10.15353/hi-am.v1i1.6800

Design of porous architectures in laser powder bed fusion: effect of hatch spacing and rotation angle on density and pore morphology

2025· article· W4415927714 on OpenAlexafffund
Rene Lam, Tomisin Oluwajuyigbe, Sagar Patel, Mohsen K. Keshavarz, Mihaela Vlasea

Bibliographic record

VenueProceedings of the Holistic Innovation in Additive Manufacturing (HI-AM) Conference · 2025
Typearticle
Language
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
FundersFedDev OntarioUniversity of Waterloo
KeywordsPorosityRotation (mathematics)Selective laser meltingImplantStress (linguistics)Laser

Abstract

fetched live from OpenAlex

Bone is a complex and hierarchical structure with the ability to provide extensive structural support to the body while also being lightweight for ease of motion. Bone can be damaged due to injury or illness, requiring the need for an orthopedic implant to enhance function, to provide structure and to encourage the growth of new bone. A challenge with current metal orthopedic implants is stress shielding, where there is a mismatch of mechanical moduli between the implant and human bone. When designing implants, it is important to tailor the mechanical response of the implant to natural bone to avoid stress shielding. This research explores a new method for implant design, incorporating pores stochastically using laser powder bed fusion (PBF-LB). This type of porosity is introduced into a solid metal part during printing by altering process parameters in PBF-LB. The density and pore morphology are dictated by the hatch spacing (100 – 500 µm) and rotation angle (60° and 67°). These structures were printed in Ti-6Al-4V. The effects of the hatch spacing and rotation angle on melt pool morphology and porosity were investigated, resulting in densities of 50.20 - 99.98% and columnar and stochastic pore morphologies.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.247
Teacher spread0.229 · 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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