INVESTIGADORES
CRAVERO Fiorella
congresos y reuniones científicas
Título:
Prediction of tensile strength at break for linear polymers applied to new materials development (5 pag)
Autor/es:
PALOMBA, DAMIAN; CRAVERO, FIORELLA; VAZQUEZ, GUSTAVO, E.; DÍAZ, MÓNICA F.
Lugar:
Santa Fe
Reunión:
Congreso; Congreso Internacional de Metalurgia y Materiales SAM-CONAMET / IBEROMAT 2014; 2014
Institución organizadora:
SAM - Asociación Argentina de Materiales
Resumen:
Often a selection between materials must be made to satisfy requirements of performance and/or cost. In approaching a design problem, the engineer will think first of desired properties of a specific material. Although the typical approach in the design of new materials has been empirical, at present there has been much progress in the knowledge of relationships between the molecular structure of a material and its properties that leads to the ability to predict material properties previous synthesis. In this way, it has led to tremendous savings in time and cost. Nevertheless, it is not easy to achieve these predictions because the variables involved are very complex from a quantitative and qualitative point of view. Mechanical properties of polymers delineate their application profile. To the best of our knowledge, this is one of the first attempts to investigate the possibility of predicting tensile properties for polymers by using QSPR (Quantitative Structure-Property Relationship) technique. In this paper we present results about the prediction of tensile strength at break for a group of amorphous synthetic and linear polymers by means the QSPR technique. The dataset was generated for this work. Simplified molecular models were designed in order to depict polymers and also new descriptors were proposed. A new QSPR model is introduced providing a tool for the design of materials with specific application profile. A combined variable selection method was applied to the whole set of descriptors with the aim of reducing the set of descriptors using a variable selection method and a physicochemical-motivated strategy. The final set consisted of 4 descriptors (2 classic and 2 chose manually) whit very good statistical performance for regard to R2 (squared correlation coefficient) and other classical statistical parameters, but due to the lack of related works in the literature could not compare performance.