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

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

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

Notice bibliographique

RevueECS Meeting Abstracts · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueDigital Transformation in Industry
Établissements canadiensConcordia University
Organismes subventionnairesnon disponible
Mots-clésManufacturing engineeringEngravingModular designElectrochemical machiningComputer scienceEngineeringNanotechnologyProcess engineeringMechanical engineeringMaterials science

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,011

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,241
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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