MétaCan
Menu
Back to cohort
Record W4386454733 · doi:10.1002/9781119210801.ch4

Directed Energy Deposition (DED)

2021· other· en· W4386454733 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
KeywordsDeposition (geology)LaserThermal conductionAbsorption (acoustics)ElectronBeam (structure)Cathode rayMaterials scienceMechanical engineeringPhysicsEngineering physicsOpticsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This chapter covers a brief description of the fundamental principles governing laser/electron beam material interaction and absorption. It focuses on physics, and governing equations of laser directed energy deposition (LDED) and electron beam directed energy deposition (EDED). Lasers and electron beams provide many advantages when it comes to the thermal material processing. A laser beam has many unique characteristics that make it a proper tool for material processing. Several aspects of electron beam interactions with materials are the same as laser material interaction; however, there are a few phenomena that need further understanding. Both LDED and EDED are complex processes that include multiple physical phenomena, such as energy absorption, heat conduction, heat losses, phase transformation, and fluid dynamics. To create a heat distribution in the DED substrate, three approaches have been made: analytical modeling, numerical modeling, and experimental-based modeling. The chapter discusses multiple models that are more popular for DED.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.113
Threshold uncertainty score0.960

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)0.0410.000

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207