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Record W6987236146

Spark Assisted Chemical Engraving: A Novel Approach for Quantifying the Machining Zone Parameters Using Drilling Forces

2014· dissertation· en· W6987236146 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2014
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMachiningWork (physics)Process (computing)Abrasive machining
DOInot available

Abstract

fetched live from OpenAlex

Glass has stirred human interest since the dawn of history due to its unique properties including its high mechanical strength, transparency, thermal and chemical properties. With the great technological advancement that we are witnessing today in the micro-technology field, glass micro-machining has already found applications in the optical, electronics, and biomedical applications. In fact, such applications require high-aspect-ratio structures of defined wall flatness and surface roughness.
\nThere exist nowadays several glass micro-machining technologies that are being developed to meet this demand. These are based on thermal (laser), chemical (dry and wet etching), and mechanical (ultrasonic, abrasive and diamond-tool drilling) processes. Spark Assisted Chemical Engraving (SACE) is a non conventional glass micro-machining technology which is based on discharge generation at the tool tip. This is known to heat up the glass surface.
\nToday, the machining mechanism is highly questionable where it is explained differently by many researchers in the field. This is due to the fact that the basic understanding about the process and the local variables in the machining zone is still missing. Although research about SACE drilling has allowed achieving deeper and smaller holes, these results remain specific to certain machining conditions. In fact, they are achieved experimentally by trial and error due to limited knowledge about the process fundamentals. Therefore, it can be said that SACE machining is still blind where the idea of doing feed-back drilling has not been explored sufficiently. These are the basic reasons of why SACE glass machining remains in laboratories and is never applied in industry.
\nThe aim of this work is to unveil basic information about the SACE machining process and the local parameters in the machining zone. For this purpose, a methodology is developed for measuring the local machining zone parameters based on the force exerted on the tool during machining. Measurement errors caused by tool bending, wear and thermal expansion are quantified and considered while measuring and analysing the machining forces. Thus, in a first step, the machining force is characterized and analysed to get a deeper understanding about its origin and the reasons of its formation. This signal is used in a second step to extract information about local variables including the machining gap size, the local glass surface temperature and the origin of its texture. Based on the understanding of the machining
\nprocess that this work brings, a thermal model is built which describes heat transfer to the glass surface. The agreement between calculations and measurements ensures the validity of both the model and measurement methodology. Based on the results, the machining mechanism is explained as a thermal assisted etching process. Machining can exist in two modes based on the electrolyte state (aqueous or molten) which depends on the local flushing.
\nForce signal readings showed that tool-glass bonding can occur during machining which may hinder the drilling progress. Based on the understanding that this work brought about the machining mechanism and the factors that influence it, force feedback algorithms are built with the aim to establish a balance between local heating and flushing. The efficiency of the various algorithms in enhancing the drilling performance was compared and assessed based on the resulting drilling time. The knowledge acquired allowed building algorithms that succeeded in drilling high-aspect-ratio holes up to 1:9 while using very small tools (70 microns diameter) without breakage. The resulting drilling time is dramatically reduced to few seconds compared to several minutes in the state-of-the-art SACE drilling.

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)0.001
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 score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.083
GPT teacher head0.317
Teacher spread0.235 · 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
Published2014
Admission routes1
Has abstractyes

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