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Record W2893150263 · doi:10.1149/ma2018-02/24/852

Electrochemical Manufacturing to Support Industry 4.0 – Spark Assisted Chemical Engraving (SACE)

2018· article· en· W2893150263 on OpenAlexaff
Lucas A. Hof, Rolf Wüthrich

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsConcordia University
Fundersnot available
KeywordsManufacturing engineeringEngravingModular designElectrochemical machiningComputer scienceEngineeringNanotechnologyProcess engineeringMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Manufacturing industry is facing new challenges as there is a growing demand for mass-personalized products at low cost. A new kind of processes need to be developed. This new revolution was recognized recently by industry and in Germany the key word industry 4.0 [1] was introduced to characterize this “fourth industrial revolution” for the entire manufacturing value chain. A major hurdle to overcome for successful production of mass personalized products are setup and tooling costs. According to recent studies (made by the Universities of Michigan and Cincinnati for the World Economic Forum) hybrid technologies, including electrochemical technologies, are promising to address these manufacturing challenges [2]. Among the established electrochemical technologies can be cited electrochemical (discharge) machining, electro-deposition/-forming and electropolishing e.g. as post-process for metal additive manufactured (AM) parts. None is yet used for mass personalisation, where the custom products are no longer an assembly of individual parts (i.e. modular design), but they are fully personalized, i.e. the shapes of the parts change too. Suitable manufacturing processes for personalized batch-size-1 production must be highly flexible and have little overhead, particularly for the tooling. Hybrid technologies are very promising as these processes require little to no specialized tooling and can handle virtually any shape, including inner surfaces. At the same time, glass, existing for millions of years in its natural form, has fascinated and attracted much interest from both the academic and industrial world. The application of glass science to the improvement of industrial tools occurred only in the past century, with a few exceptions. Glass has been employed in many forms to fabricate glazing and containers for centuries while it is now entering new applications that are appearing in micro and even nanotechnology like fibers, displays and Micro-Electro-Mechanical-System (MEMS) devices [3]. Many qualities make glass attractive since it is transparent, chemically inert, environmentally friendly and its mechanical strength and thermal properties. In fact, no other materials being mass-produced have shown such qualities over so many centuries. Nowadays glass offers recycling opportunities and allows for tailoring new and dedicated applications. Moreover, glass is radio frequency (RF) transparent, making it an excellent material for sensor and energy transmission devices. Another advantage of using glass in microfluidic MEMS devices [4] is its relatively high heat resistance, which makes these devices suitable for high temperature microfluidic systems [5] and sterilization by autoclaving. However, glass is a hard to machine material, due to its hardness and brittleness. Machining high-aspect ratio structures is still challenging due to long machining times, high machining costs and poor surface quality [6]. Hybrid methods like Spark Assisted Chemical Engraving (SACE) [7] perform well to machine high aspect ratio and smooth surface structures on glass. These assets of SACE technology combined with its relative high machining speeds compared to chemical methods and low-cost compared to femto-laser technologies make SACE perfectly suitable for rapid prototyping of micro-scale glass devices. In this thermochemical process, a voltage is applied between tool- and counter-electrode dipped in an alkaline solution (typical NaOH or KOH). At high voltages (around 30 V), the bubbles evolving around the tool electrode coalesce into a gas film and discharges occur from the tool to the electrolyte through it. Glass machining becomes possible due to thermally promoted etching (breaking of the Si-O-Si bond) [7]. In the present communication, it is shown how electrochemical processes can be used to design new high precision manufacturing processes for industry 4.0. In particular hybrid machining by SACE technology is discussed for hard-to-machine materials like glass. Some other examples are highlighted as well in the field of post-processing technologies for metal AM parts and fabrication of high-precision complex metal structures based on 3D printed high resolution polymer models. [1] Deloitte, “Industry 4.0. Challenges and solutions for the digital transformation and use of exponential technologies”, Deloitte, pp. 1–30, 2015. [2] J. Ni, J. Lee “Emerging and Disruptive Technologies for the Future of Manufacturing” Case study no.7 World Economic Forum Global Agenda Council on the Future of Manufacturing [3] E. Le Bourhis, “Glass, Mechanics and Technology.”, Wiley-VCH, 2014. [4] G. M. Whitesides, “The origins and the future of microfluidics.”, Nature, vol. 442, no. 7101, pp. 368–373, 2006. [5] D. Sinton, “Energy: the microfluidic frontier.”, Lab Chip, vol. 14, no. 17, 2014. [6] L. Hof, J. D. Abou Ziki, “Micro-hole drilling on glass substrates – a review”, Micromachines, vol. 8, no.53, 2017. [7] R. Wüthrich and J. D. Abou Ziki, “Micromachining Using Electrochemical Discharge Phenomenon.”, Elsevier, 2015. Figure 1

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.011

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 head0.241
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2018
Admission routes1
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