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Record W4247420218 · doi:10.32920/ryerson.14647383.v1

Design and optimization of CNC based polishing process

2021· preprint· en· W4247420218 on OpenAlexaboutno aff
James Lambie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAerospaceManufacturing engineeringPolishingTaguchi methodsProcess (computing)Quality (philosophy)EngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

In recent years many aerospace manufacturing corporations have taken an initiative to eliminate the manual interaction of workers and moving work pieces. At the same time there is an ever-increasing demand for efficiency in the manufacturing sector. The research performed investigates the design and development of passive compliance polishing tools for both external and internal polishing of parts on a CNC turning center. The goal of this thesis is to develop tools that meet new safety demands while at the same time optimizing the tools through the Taguchi method and process planning in order to meet quality standards cost effectively. Experiments on both test pieces and production parts were conducted not only at Ryerson University but at Messier-Dowty as well.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score0.682

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2021
Admission routes1
Has abstractyes

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