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Record W4386454720 · doi:10.1002/9781119210801.ch9

Additive Manufacturing of Metal Matrix Composites

2021· other· en· W4386454720 on OpenAlexaff
Ehsan Toyserkani, Dyuti Sarker, Osezua Ibhadode, Farzad Liravi, Paola Russo, Katayoon Taherkhani

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceFabricationPowder metallurgyCeramicComposite materialComposite numberCastingDeposition (geology)Phase (matter)AluminiumMatrix (chemical analysis)MetallurgyMicrostructure

Abstract

fetched live from OpenAlex

This chapter helps the students to understand the concept of Metal Matrix Composites (MMCs), and to learn about applications of additive manufacturing (AM) technology in the fabrication of MMCs and possible challenges. There is a wide variety of manufacturing techniques available for MMCs. Based on the processing temperature of the matrix material, the conventional techniques of MMCs are generalized into four as follows: liquid-phase methods, solid-phase methods, solid/liquid dual-phase methods, and deposition methods. The chapter emphasizes the fabrication of MMCs through powder-based AM techniques. The method of composite manufacturing using AM can be accomplished through either the material deposition or a hybrid process, where the mixture of various materials can be done before AM. The chapter presents various particulate reinforced ferrous, titanium, aluminum, and nickel matrix composites manufactured through AM techniques. The most common manufacturing methods of ceramic particle-strengthened t aluminum matrix composites are mechanical alloying, powder metallurgy, and stir casting.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.673
Threshold uncertainty score1.000

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)0.0110.000

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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