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Record W3094322745 · doi:10.3390/jmmp4040099

The Capabilities of Spark-Assisted Chemical Engraving: A Review

2020· review· en· W3094322745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2020
Typereview
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMachiningSurface micromachiningEngravingElectrical discharge machiningMechanical engineeringBrittlenessMaterials scienceSPARK (programming language)Engineering drawingEngineeringComputer scienceFabricationComposite material

Abstract

fetched live from OpenAlex

Brittle non-conductive materials, like glass and ceramics, are becoming ever more significant with the rising demand for fabricating micro-devices with special micro-features. Spark-Assisted Chemical Engraving (SACE), a novel micromachining technology, has offered good machining capabilities for glass and ceramic materials in basic machining operations like drilling, milling, cutting, die sinking, and others. This paper presents a review about SACE technology. It highlights the process fundamentals of operation and the key machining parameters that control it which are mainly related to the electrolyte, tool-electrode, and machining voltage. It provides information about the gas film that forms around the tool during the process and the parameters that enhance its stability, which play a key role in enhancing the machining outcome. This work also presents the capabilities and limitations of SACE through comparing it with other existing micro-drilling and micromachining technologies. Information was collected regarding micro-channel machining capabilities for SACE and other techniques that fall under four major glass micromachining categories—mainly thermal, chemical, mechanical, and hybrid. Based on this, a figure that presents the capabilities of such technologies from the perspective of the machining speed (lateral) and resulting micro-channel geometry (aspect ratio) was plotted. For both drilling and micro-channel machining, SACE showed to be a promising technique compared to others as it requires relatively cheap set-up, results in high aspect ratio structures (above 10), and takes a relatively short machining time. This technique shows its suitability for rapid prototyping of glass micro-parts and devices. The paper also addresses the topic of surface functionalization, specifically the surface texturing done during SACE and other glass micromachining technologies. Through tuning machining parameters, like the electrolyte viscosity, tool–substrate gap, tool travel speed, and machining voltage, SACE shows a promising and unique potential in controlling the surface properties and surface texture while machining.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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