Prediction of Failure Categories in Plastic Extrusion Process with Deep Learning

Fatma DEMİRCAN KESKİN; Ural ÇİÇEKLİ; Doğukan İÇLİ

doi:10.38016/jista.878854

Prediction of Failure Categories in Plastic Extrusion Process with Deep Learning

Journal of Intelligent Systems: Theory and Applications ◽

10.38016/jista.878854 ◽

2022 ◽

Vol 5 (1) ◽

pp. 27-34

Author(s):

Fatma DEMİRCAN KESKİN ◽

Ural ÇİÇEKLİ ◽

Doğukan İÇLİ

Keyword(s):

Deep Learning ◽

Extrusion Process

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A morphological and energy-dispersive x-ray spectroscopy (EDS) investigation of separator-grade cellophane

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100169882 ◽

1994 ◽

Vol 52 ◽

pp. 430-431

Author(s):

Michael E. Rock ◽

Vern Kennedy ◽

Bhaskar Deodhar ◽

Thomas G. Stoebe

Keyword(s):

Extrusion Process ◽

Morphological Characterization ◽

Energy Dispersive ◽

Regenerated Cellulose ◽

Battery System ◽

X Ray ◽

Functional Differences ◽

Transmission Electron ◽

Separator Material ◽

Underwater Target

Cellophane is a composite polymer material, made up of regenerated cellulose (usually derived from wood pulp) which has been chemically transformed into "viscose", then formed into a (1 mil thickness) transparent sheet through an extrusion process. Although primarily produced for the food industry, cellophane's use as a separator material in the silver-zinc secondary battery system has proved to be another important market. We examined 14 samples from five producers of cellophane, which are being evaluated as the separator material for a silver/zinc alkaline battery system in an autonomous underwater target vehicle. Our intent was to identify structural and/or chemical differences between samples which could be related to the functional differences seen in the lifetimes of these various battery separators. The unused cellophane samples were examined by transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDS). Cellophane samples were cross sectioned (125-150 nm) using a diamond knife on a RMC MT-6000 ultramicrotome. Sections were examined in a Philips 430-T TEM at 200 kV. Analysis included morphological characterization, and EDS (for chemical composition). EDS was performed using an EDAX windowless detector.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Modeling and Control of a Food Extrusion Process

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00075-6 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S361-S366 ◽

Cited By ~ 1

Author(s):

J Elsey

Keyword(s):

Extrusion Process ◽

Modeling And Control ◽

Food Extrusion ◽

And Control

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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