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Record W4245983590 · doi:10.32920/ryerson.14646651

Micro-channel milling using abrasive waterjets and high pressure abrasive slurry jets

2021· preprint· en· W4245983590 on OpenAlexfundno aff
Naser Haghbin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNozzleAbrasiveMaterials scienceSlurryJet (fluid)MachiningComposite materialMetallurgyMechanical engineeringMechanics

Abstract

fetched live from OpenAlex

Abrasive water jet technology can be used for micro-milling using recently developed miniaturized nozzles. This thesis develops methodologies to predict the shape of micro-channels milled using high pressure abrasive water jets, and presents a new high pressure abrasive slurry jet micro-machining process. Since abrasive water jet (AWJ) machining is often used with both the nozzle tip and workpiece submerged in water to reduce noise and contain debris, the performance of submerged and unsubmerged abrasive water jet micro-milling of channels in 316L stainless steel and 6061-T6 aluminum at various nozzle angles and standoff distances were compared. It was found that the centerline erosion rate decreased with channel depth due to the spreading of the jet as the effective standoff distance increased, and because of the growing effect of the stagnation zone as the channel became deeper. The erosive jet spread over a larger effective footprint in air than in water, since particles on the jet periphery were slowed much more quickly in water due to increased drag. As a result, the width of a channel machined in air was wider than that in water. It was also found that the erosive efficacy distribution changed suddenly after the initial formation of the channel. Then, a new surface evolution model was developed that predicts the size and shape of relatively deep micro-channels up to aspect ratios of 3 resulting from unsubmerged and iv submerged abrasive water jet micro-machining (AWJM) using a novel approach in which two different erosive efficacy expressions were sequentially applied. Since the channels produced by AWJM were found to be relatively wavy due to fluctuations in abrasive mass flow rate, a novel high pressure (water pump pressure up to 345 MPa) abrasive jet slurry micro-machining (HASJM) system was introduced by feeding a premixed slurry into the mixing chamber of a water jet machine with a micro-nozzle. Moreover, an existing model developed for AWJM abrasive particle velocities was modified and used to predict the particle velocity in HASJM, and then verified using a double disc apparatus (DDA). The HASJM system was then used to study the effect of entrained air in abrasive water jet micro-machining (AWJM) by performing experiments at the same particle velocity and dose for the two systems. The centerline waviness, Wa, of micro-channels made in SS316L and Al60661-T6 using HASJM were typically 3.4 times lower than those made with AWJM using the same dose of particles due to the more constant abrasive flow rate provided by the HASJM provided. The centerline roughness, Ra was approximately the same in both processes at a traverse velocity of Vt=4572 mm/min and a nozzle angle of 90°. For micro-channels of a given depth, the widths of those made with HASJM were 25.6 % narrower than those produced with AWJM, mainly due to the wider jet that resulted from the entrained air in AWJM.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.251
Teacher spread0.232 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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