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Analysis of Stresses in CFRP Composite Plates Drilled with Conventional and Abrasive Water Jet Machining

2013· article· en· W2085599207 on OpenAlexafffund
Muhammad Umair Saleem, Habiba Bougherara, Lotfi Toubal, François Cénac, Rédouane Zitoune

Bibliographic record

VenueMaterials science forum · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsUniversité du Québec à Trois-RivièresToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMachiningMaterials scienceThermographyComposite materialAbrasiveComposite numberStiffnessStress (linguistics)InfraredMetallurgyOptics

Abstract

fetched live from OpenAlex

The aim of this paper is to analyze the influence of two machining processes on the mechanical behaviour of composite plates under cyclic loading. For this purpose, an experimental study using several CFRP plates drilled with conventional machining and non-conventional machining (abrasive water jet) was carried out. Digital image correlation and static tests using an Instron 4206 tester were performed. In addition, infrared thermography (IR) and fatigue tests were also performed to assess temperature and damage evolutions and also the stiffness degradation. Fatigue results have shown that the damage accumulation in specimens drilled with conventional machining was higher than the abrasive water jet ones. Furthermore, the endurance limit for plates drilled conventionally was approximately 10% higher than those drilled with abrasive water jet. This difference was related to the initial surface integrity after machining induced by the difference in the mechanism of material's removal between the two processes. The difference in surface texture was responsible for the initiation of stress concentration sites as evident from IR camera’s stress analysis. This was confirmed by SEM tests conducted after a destructive sectioning of the specimens before fatigue testing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.995

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.005
GPT teacher head0.221
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 designObservational
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

Citations10
Published2013
Admission routes2
Has abstractyes

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