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A Review of Metal Additive Manufacturing Technologies

2018· review· en· W2884812296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2018
Typereview
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceInconelMetallurgyMicrostructureAerospaceAluminiumNickelAlloyEngineering

Abstract

fetched live from OpenAlex

Additive manufacturing is a layer based manufacturing process aimed at producing parts directly from a 3D model. This paper provides a review of key technologies for metal additive manufacturing. It focuses on the effect of important process parameters on the microstructure and mechanical properties of the resulting part. Several materials are considered including aerospace alloys such as titanium (TiAl6V4 “ UNS R56400 ”), aluminum (AlSi10Mg “ UNS A03600 ”), iron-and nickel-based alloys (stainless steel 316L “ UNS S31603 ”, Inconel 718 “ UNS N07718 ”, and Invar 36 FeNi36 “ UNS K93600 ”).

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0070.014
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.058
GPT teacher head0.321
Teacher spread0.263 · 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