Injection molding product weight : Online prediction and control based on a nonlinear principal component regression model
- Author
- YI YANG1 ; FURONG GAO1
[1] Department of Chemical Engineering, The Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong-Kong - Source
Polymer engineering and science. 2006, Vol 46, Num 4, pp 540-548, 9 p ; ref : 23 ref
- CODEN
- PYESAZ
- ISSN
- 0032-3888
- Scientific domain
- Polymers, paint and wood industries
- Publisher
- Wiley Subscription Services, Hoboken, NJ
- Publication country
- United States
- Document type
- Article
- Language
- English
- Keyword (fr)
- Analyse composante principale Analyse régression Commande boucle fermée Commande non linéaire Commande processus Commande prédictive En ligne Etude théorique Modélisation Moulage injection Poids Polymère
- Keyword (en)
- Principal component analysis Regression analysis Closed feedback Non linear control Process control Predictive control On line Theoretical study Modeling Injection molding Weight Polymer
- Keyword (es)
- Análisis componente principal Análisis regresión Bucle realimentación cerrada Control no lineal Control proceso Control predictiva En línea Estudio teórico Modelización Moldeo por inyección Peso Polímero
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D10 Polymer industry, paints, wood / 001D10A Technology of polymers / 001D10A04 Machinery and processing / 001D10A04A Plastics / 001D10A04A2 Moulding / 001D10A04A2A Injection moulding
- Discipline
- Polymer industry, paints, wood
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 17639958
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1002/pen.20522 
https://dx.doi.org/10.1002/pen.20522
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