Multivariate Analysis of Composition Features to Perform Linear Predictions of Rubber Blends Tensile Strength

• Published in: Applied Mechanics and Materials Vol. 872; pp. 77 - 82
• Main Authors: Jimbert, Pello, Guraya, Teresa, Fernandez Martinez, Roberto, Okariz, Ana
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.10.2017
• Subjects: Composition; Design of experiments; Mathematical models; Mechanical properties; Mixtures; Model accuracy; Multivariate analysis; Regression analysis; Rubber; Taguchi methods; Tensile strength; Tensile Strength; Computational Linear Modeling; Taguchi Design of Experiment; Multivariate Data Analysis; Rubber Blend Composition
• ISBN: 9783035711660; 3035711666
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.872.77

The goal in this work is to build a multivariate linear model to predict tensile strength since is one of the most significant mechanical properties of carbon-black reinforced rubber blends. This model is based in the relationship between the final mechanical properties and the material composition, with the advantage of using this model to improve the design of the composition of the blend. In order to predict this relevant physical attribute of rubber blends a linear regression is performed, but previously a multivariate analysis of the data is done to get a better accuracy in the validation of the model. The number of used instances and the values are determined by a Taguchi design of experiments, and the output values are obtained from the tensile strength test following the corresponding standard. After the performance of the multivariate analysis where the input variables are under a detail study, a selection of the best features help to improve the accuracy of the model, passing from a 24.80% to a 20.60% of error.