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Record W4386454700 · doi:10.1002/9781119210801.ch5

Powder Bed Fusion Processes

2021· other· en· W4386454700 on OpenAlexaff
Ehsan Toyserkani, Dyuti Sarker, Osezua Ibhadode, Farzad Liravi, Paola Russo, Katayoon Taherkhani

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermal conductionHeat transferFusionLattice Boltzmann methodsShrinkageProcess (computing)PhysicsMechanicsLattice (music)Boltzmann equationMaterials scienceStatistical physicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

This chapter helps the students to gain a clear understanding of the physics behind the powder bed fusion (PBF) processes. It identifies the main governing equations for heat transfer by conduction and fluid flow in the melt pool. The chapter also helps to gain an understanding of the numerical models and techniques employed in the description of the heat transfer during laser PBF. There are substantial similarities between the physics and governing equations of directed energy deposition processes, and physics and governing equations of PBF processes. The chapter discusses two main PBF processes: LPBF and electron beam PBF (EB-PBF). In the LPBF process, four stages can be identified based on the laser–powder bed interaction: absorption, heat conduction, melting and shrinkage, and resolidification. The Lattice Boltzmann method was introduced to be very useful for modeling melt pool evolution during the EB-PBF process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.006

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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