| Titre : |
Improving composite manufacturing reliability and yield with affordable models and simulations |
| Type de document : |
texte imprimé |
| Auteurs : |
Elena Syerko, Auteur ; Christophe Binetruy, Auteur ; Pavel Simacecek, Auteur ; Suresesh Advani, Auteur |
| Année de publication : |
2018 |
| Article en page(s) : |
p. 58-62 |
| Note générale : |
Bibliogr. |
| Langues : |
Anglais (eng) |
| Catégories : |
Automatisation
Capteurs (technologie)
Composites thermoplastiques -- Moulage par injection
Fiabilité
Logiciels
Moulage de composites liquides
Moulage par transfert de résine sous vide
Perméabilité
Simulation par ordinateur
Textiles et tissus à usages techniques
|
| Index. décimale : |
668.4 Plastiques, vinyles |
| Résumé : |
Composite manufacturing still often relies on trial-and-error approaches to design the process, making little use of the plethora of science-based literature, models and simulations. This paper presents an approach for the development of easy-to-use tools with no learning curve, with lower licensing costs and can provide useful information to manufacturing engineers or technicians, helping them improve their yield and reliability with fewer trials. Examples of such tools are presented for liquid composite moulding processes, but can be extended to other processes. |
| Note de contenu : |
- Approach
- Image-based fibre perform permeability prediction tool
- Web-based tools and software "calculators"
- Automated tools
- Workforce training
- Consulting
- Fig. 1 : Liquid Injection Moulding Simulation (LIMS software) can predict vent locations for successful mould filling in a complex mould, which may trigger race tracking along edges and corners resulting in 1024 possible flow patterns
- Fig. 2 : Image-basee fabric permeability prediction tools (Ge-Mage-Flow3D software). After one has the values of the permeability of fabrics, that can be used as input for mould filling simulations
- Fig. 3: Sequential VARTM infusion
- Fig. 4 : Automated process analysis system. Depending on the levet of uncertainty, the user provides the available part data and receives encapsulated output. Software controllers convert the data, execute the simulation and extract the crucial information from the results. The operations of the software controller (s) and simulation software are transparent to the user. Note that: 11 the full simulation data can still be stored and reviewed, and (**) as the current implementation [4, 9, 10] is to serve the part designer, a simple cost analysis is possible
- Fig. 5 : Conceptual schematic from CAD to automated manufacturing using sensors and simulations |
| En ligne : |
https://drive.google.com/file/d/1J0ypf2DDAYtlADcduRJTGKjb558ZO1Ko/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=30478 |
in JEC COMPOSITES MAGAZINE > N° 120 (04/2018) . - p. 58-62
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Improving composite manufacturing reliability and yield with affordable models and simulations in JEC COMPOSITES MAGAZINE, N° 120 (04/2018)
