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Record W4293486604 · doi:10.21203/rs.3.rs-1977068/v1

A novel method by microwave cutting ceramics based on thermal crack and trajectory control

2022· preprint· en· W4293486604 on OpenAlexafffund
Yao Lu, Zongyang He, Jianan Xu, Yang Wang, LiJun Yang

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMaterials scienceGraphiteComposite materialCeramicMachiningCoatingBrittlenessMicrowaveThermalUltimate tensile strengthThermal shockMetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract Herein, microwave thermal crack method is innovatively employed in cutting Al 2 O 3 ceramics. Different from the conventional cutting technology, thermal controlled fracture method is progressive and environmentally friendly, which adopts tensile stress to peel off the brittle material into two parts. The heat source induces tensile stress as thermal stress. Additionally, based on Fourier heat transfer equation and thermo-elasticity, the physical model of microwave thermal crack cutting Al 2 O 3 ceramics is established and calculated. The thickness of the graphite coating, the width of the graphite coating and the prefabricated crack in the process of microwave thermal crack cutting Al 2 O 3 were studied. The effect of graphite coating width on crack trajectory was also investigated by simulation combined with test. From the result, it can be seen that the narrower the width of graphite coating, the weaker the processing ability and the better the trajectory control ability. The microscope is adopted to evaluate the surface and cross section morphologies detailly. This study elaborates briefly the interaction mechanism of microwave thermal crack cutting Al 2 O 3 ceramic surface and provides practical guidance for aerospace industries applications.

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)0.007
metaresearch head score (Gemma)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.376
Teacher spread0.331 · 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 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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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