Predicting viscosity in polyurethane polymerization for liquid composite molding using neural networks and surface methodology

The tuning of the viscosity of polyurethane (PU) during its polymerization enables its use in different applications including the liquid composite molding (LCM). In this work, the rheological behavior during polymerization of PU from castor oil (CO), polyether (PE), and a mixture of both polyols (C...

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Bibliographic Details
Published in:Polymer bulletin (Berlin, Germany) Germany), 2024-06, Vol.81 (9), p.8341-8358
Main Authors: Cruz, Joziel Aparecido da, Ornaghi, Heitor Luiz, Amico, Sandro Campos, Bianchi, Otávio
Format: Article
Language:English
Subjects:
Artificial neural networks
Castor oil
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Complex Fluids and Microfluidics
Data analysis
Formulations
Impact strength
Kinetics
Liquid composite molding
Mixtures
Molecular weight
Neural networks
Organic Chemistry
Original Paper
Physical Chemistry
Polymer Sciences
Polymerization
Polymers
Polyols
Polyurethane resins
Response surface methodology
Rheological properties
Rheology
Rheometry
Soft and Granular Matter
Viscoelasticity
Viscosity
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Summary:The tuning of the viscosity of polyurethane (PU) during its polymerization enables its use in different applications including the liquid composite molding (LCM). In this work, the rheological behavior during polymerization of PU from castor oil (CO), polyether (PE), and a mixture of both polyols (COPE, 50/50 wt%) was investigated using oscillatory rheometry at different isotherms (40, 50, 60, 70, and 80 °C). Approaches based on artificial neural networks (ANNs) and response surface methodology (RSM) were used for data analysis and to obtain prediction tools related to the PU polymerization. This approach can contribute to experimentally estimate new formulations to obtain information that kinetic models based on conversion rates are unable to provide. The polymerized PU(CO), PU(COPE), and PU(PE) compositions showed flexible phase T g of 15, − 13, and − 30 °C, respectively. Due to the greater reactivity of the PU(CO), its viscosity increased faster than that of PU(PE). The balance of properties obtained with the mixtures of polyols resulted in viscosity values of 300, 500, and 1000 mPa s, which are suitable for the LCM. This approach shows the combination of ANN and RSM can be advantageous to complement the kinetics analysis of PU resins with tunable properties for the LCM. Highlights Rheokinetics of PUs analyzed using oscillatory rheometry. Experimental data examined with neural networks and response surface methodology. Successful utilization of ANN and RSM for predicting PU polymerization. Combined ANN and RSM enhance kinetics analysis of PU resins. This approach reveals viscosity values suitable for composite liquid molding.
ISSN:0170-0839
1436-2449
DOI:10.1007/s00289-023-05117-5