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Record W2552093855

Processing, properties and prospects for melt infiltrated (MI) SiCf-ceramic composites

2015· article· en· W2552093855 on OpenAlexaboutno aff
R. N. Singh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialChemical vapor infiltrationCeramicCeramic matrix compositeSilicon carbideComposite numberBrittlenessPorosityToughnessNear net shape
DOInot available

Abstract

fetched live from OpenAlex

Ceramic materials exhibit superior mechanical properties at high temperatures. But, their use as structural components is severely limited because of their brittleness. Fiber-reinforced ceramic matrix composites (FRCMC), by incorporating fibers in ceramic matrices, not only exploit their attractive high-temperature strength but also enhance their toughness thereby rendering these attractive for many applications. Some of these applications require an understanding of the interrelationship among processing, fiber-matrix interface and mechanical properties both at room and elevated temperatures. Processing of FRCMC is typically done by chemical vapor infiltration (CVI), filament winding and hot-pressing, and polymer infiltration and pyrolysis (PIP) approaches. Most of these approaches do not lead to full-density unless external pressure is applied during processing. A novel approach of melt-infiltration (MI) is a promising technique for fabricating fully dense and net-shape SiC fiber-reinforced SiC composites. The processing of FRCMC by MI was pioneered, invented and developed for making fully dense, netand complex-shape silicon carbide (SiC) fiber-reinforced ceramic matrix composites [1]. The melt infiltration process technology exhibits processing-simplicity in which a porous preform consisting of carbon, SiC particulates and reinforcing SiC fibers is infiltrated with molten silicon (Si) for in-situ formation of SiC. This innovative processing is unique in that it is complete in a few minutes and produces net-shape and fully-dense composites inexpensively compared to other ceramic composite processes requiring applied pressure at very high temperatures for densification leading to shape change upon consolidation. The high temperature mechanical properties of MI composites may be limited by the properties of the fiber and Si-SiC matrix. In particular, the matrix properties depend on the amount of Si in the Si-SiC matrix phase. Therefore, Si-SiC composites containing 10-45 vol% Si were fabricated using melt-infiltration process and their microstructure and mechanical properties were measured between 1250-1550°C. Elastic modulus, strength, and creep behaviors were studied including the influence of the Si content on these properties. Elastic modulus and strength were shown to decrease with increases in both Si content and temperature. The composites containing large amounts of the continuous Si phase exhibited extremely low strength near the melting point of Si, while the composites containing small amounts of Si with a continuous SiC network retained 120 to 200 MPa strength even above the melting point of Si. The composites containing 10 and 20 vol% Si exhibited excellent creep resistance up to 1550 C, well above the melting point of silicon. Creep rates as low as 10 -10 to 10 -9 were obtained at stresses of 40 to 75% of the strengths. Microstructural examination of the crept composites with 36 and 45 vol% Si showed a correlation between the creep behavior and nucleation and linkage of cavity in the tensile side leading to tertiary creep and creep failure. These results will be discussed and presented. Biographical Sketch Dr. Raj N. Singh is currently Regents Professor, Williams Companies Distinguished Chair Professor, Director Energy Technologies Programs and Head of School of Materials Science and Engineering at Oklahoma State University (OSU). He received his Sc.D. degree from Massachusetts Institute of Technology, M.S. from University of Manitoba and B.S. from IIT Kanpur all in Materials Science and Engineering. He worked for several years at Argonne National Laboratory, GE-RD Albert Sauveur Achievement Award of ASM International (2016); Regents Professor (OSU 2015); Fellow of the ASM International (1996); Fellow of the American Ceramic Society (1992); Fellow of Graduate School (UC 2007); Whitney Gallery of Technical Achievers GE-CRD Publication Awards GECRD Patent Awards GE-CR&D: Bronze, Silver, and Gold Patent Medallions (1983, 1987, 1988). He also serves as member of editorial boards of 5 international journals.

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 categoriesnone
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 score0.025
Threshold uncertainty score0.618

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)0.0000.000

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 head0.047
GPT teacher head0.257
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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