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Record W4233544993 · doi:10.32920/ryerson.14649972

Abrasive air jet micro-machining of highly curved surfaces

2021· preprint· en· W4233544993 on OpenAlexfundno aff
Ali Nouhi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMachiningJet (fluid)NozzleMaterials scienceMechanical engineeringConical surfaceAbrasiveAerodynamicsBrittlenessOpticsMechanicsComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

In the abrasive jet micro-machining (AJM) process, a jet of high-speed particles is directed through a micro-nozzle which is used to erode a wide variety of materials. The micro-machining of small curved devices made of brittle and ductile materials is required in optical and biomedical equipment. This dissertation aims at fabricating axial grooves and helical micro-channels in stationary and rotating curved targets, respectively, using AJM. In addition, a model is proposed to predict the shape of machined channel profiles in glass and PMMA rods. Since the air driven jet is divergent, the edges of the desired features are usually defined using a mask which is attached to the surface of the target material. This thesis presents an alternate technique using shadow masks that can be moved over the surface. It is demonstrated that this apparatus can be used to direct write features on the surface. This dissertation proposes a modification to the existing surface evolution models for predicting the channel profiles machined on highly curved and tilted surfaces. It is shown that considering the change in local nozzle standoff and the divergence angle of each particle trajectory in the jet plume results in more accurate predictions. Computational fluid dynamics (CFD) modeling showed that the jet footprint difference on the flat and curved surfaces was not due solely to the expected conical divergence in the jet, but also due to differences in the erosion caused by secondary impacts of rebounding particles. This observation has important implications for the surface profile modeling of curved surfaces. Finally, a model for the prediction of the volumetric removal of material during the machining of rotating and translating PMMA and glass rods is presented. In addition, a new experimental procedure is proposed for machining helical micro-channels in glass and PMMA rods using a cylindrical steel spring as a mask. This method of machining provides a convenient means of fabricating helical micro-channels with different aspect ratios and radii of curvature means of fabricating helical micro-channels with different aspect ratios and radii of curvature. Such helical micro-channels may have applications in inertial microfluidic devices where they can be used to aid liquid mixing and the separation of particles from a flow.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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