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[article]
Titre : Big data comes to coatings : Novel formulation optimisation using big data, modelling, and predictive tools Type de document : texte imprimé Auteurs : Partha Majumdar, Auteur ; Jonathan DeRocher, Auteur ; Michael Tran, Auteur ; Nipun Bisht, Auteur ; Rose Bohling, Auteur ; Adeline Ossola, Auteur ; Ivan Boronat Monfort, Auteur ; Philip Harsh, Auteur ; James Bohling, Auteur ; Jeff Sweeney, Auteur Année de publication : 2021 Article en page(s) : p. 40-47 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Agent mattant
Analyse des données
Données massives
Essais (technologie)
Formulation (Génie chimique)
Plan d'expérience
Revêtements organiques
Simulation prédictiveIndex. décimale : 667.9 Revêtements et enduits Résumé : Big data analytics and machine learning (ML) have disrupted nearly every industry, and are now being embraced by the coatings sector, whose formulations offer an almost infinite formulation landscape. This paper reviews recent work in this area, explores the use of iterative experimental design to map the performance of a matting resin and discusses the use of advanced data analysis through the application of applying various algorithms. Note de contenu : - EXPERIMENTAL : Materials
- FORMULATIONS
- TEST METHODS : Block - Tack - Coefficient of friction - Scrub - Stain
- OUTLINE OF DESIGN OF EXPERIMENTS (DOE)
- DATA ANALYSIS AND MODELS
- OPTIMISATION
- MODEL DEPLOYMENT
- Fig. 1 :Distribution of gloss, scrub, and scatter plot of gloss vs. scrub with CoF overlay
- Fig. 2 : Plots of LSMeans for comparing the impact of matting additives on (a) scrub and (b) CoF in white base, no extender formulations and the impact of both matting additives and extenders on (c) scrub and (d) CoF in white base with extender formulations
- Fig. 3 : Plots of LSMeans for comparing the impact of matting agents on (a) tack, (b) CoF, and (c) scrub in deep base, no extender formulations
- Fig. 4 : Plots of LSMeans for comparing the impact of interactions between matting agents and extenders on (a) tack, (D) CoF, and (c) scrub in deep base with extender formulations
- Fig. 5 : Expansion of initial input parameters from material type to characteristic properties
- Fig. 6 : Optimised formulation maps (a) all properties are equally important and (b) gloss, scrub, and CoF are twice as important as AKU and stain
- Fig. 7 : Interactive formulation map implemented in Plotly Dash and deployed in RStudio
- Table 1 : Typical white base formulations
- Table 2 : Typical deep base formulation
- Table 3 : Design variables including ingredient types and levels of interest
- Table 4 : Model R2 values of key paint properties
- Table 5 : Training and Test R2 values for the combined white base data set comparing methodologies applied to model paint properties using material property input parametersEn ligne : https://drive.google.com/file/d/1jepJbW7hRp6AOq_tJ4cjI3SSB2OMKBfh/view?usp=drive [...] Format de la ressource électronique : Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=36482
in EUROPEAN COATINGS JOURNAL (ECJ) > N° 11 (11/2021) . - p. 40-47[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 23041 - Périodique Bibliothèque principale Documentaires Disponible ICME and materials data management for more agile design and engineering of advanced materials in JEC COMPOSITES MAGAZINE, N° 134 (05-06/2020)
[article]
Titre : ICME and materials data management for more agile design and engineering of advanced materials Type de document : texte imprimé Année de publication : 2020 Article en page(s) : p. 35-36 Langues : Anglais (eng) Catégories : Composés de moulage en feuille
Composites
Conception assistée par ordinateur
Conception technique
Gestion de données
Logiciels
Simulation par ordinateur
Simulation prédictiveIndex. décimale : 668.4 Plastiques, vinyles Résumé : Reducing development times and costs of composites materials is high on the agenda for all manufacturers to remain competitive and penetrate the market. A powerful approach to achieve tis in Integrated Computational Materials Engineering (ICME) - or in simple words - an integrative design approach where materials, processing and final products are connected thourgh various scales. When organizations apply this method to composite materials, they quickly realize the amount of materials data that needs to be captured and validated, whether generated from virtual or physical testing. Note de contenu : - Advanced materials modelling and virtual allowables
- A platform to capture all physical and virtual composites information
- Fig. 1 : Composite ICME integrated materials data management at every scale considered
- Fig. 2 : Full-field homogenization with Digimat to predict composite failure envelopes
- Fig. 3 : Validation of UD strength predictions with Digimat 2019.1
- Fig. 4 : Example of managing both physical and virtual dataPermalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=35375
in JEC COMPOSITES MAGAZINE > N° 134 (05-06/2020) . - p. 35-36[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21794 - Périodique Bibliothèque principale Documentaires Disponible