Optimization of properties in a rubber compound containing a ternary polymer blend using response surface methodology

In order to find the best combination of three synthetic rubbers, that is, styrene-butadiene rubber (SBR) grade 1712, SBR grade 1721 and high-1,4-cis polybutadiene, that produce a compound with specific end-use properties, a statistical experimental design is proposed in this work. The design consis...

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Bibliographic Details
Authors: Salvatori, Pablo Ernesto, Sánchez, Gastón, Lombardi, Aldo, Nicocia, Esteban, Bortolato, Santiago Andres, Boschetti, Carlos Eugenio
Format: article
Status:Published version
Publication Date:2018
Country:Argentina
Institution:Consejo Nacional de Investigaciones Científicas y Técnicas
Repository:CONICET Digital (CONICET)
Language:English
OAI Identifier:oai:ri.conicet.gov.ar:11336/86760
Online Access:http://hdl.handle.net/11336/86760
Access Level:Open access
Keyword:BLENDS
MANUFACTURING
MECHANICAL PROPERTIES
RUBBER
THERMAL PROPERTIES
https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
Description
Summary:In order to find the best combination of three synthetic rubbers, that is, styrene-butadiene rubber (SBR) grade 1712, SBR grade 1721 and high-1,4-cis polybutadiene, that produce a compound with specific end-use properties, a statistical experimental design is proposed in this work. The design consists of ten mixtures containing specific amounts of total styrene and BR content. A number of properties are tested in each mixture, selecting those related to requirements for the tread of a high performance tire: glass transition temperature (Tg), the ratio between the viscous modulus and the elastic modulus (tanδ@60 °C), Mooney viscosity, and the tensile properties. The values obtained for each property are fit to statistically significant models, obtaining the respective response surfaces. These are next used to define a desirable formulation with the optimal ratio of each rubber, and finally the optimized formulation is validated by comparing the experimental and predicted values for each modeled property.