Predicting physico-mechanical properties of LLDPE and PDMS rubber blends using ANFIS-based model

Abstract

Blends of linear low density polyethylene (LLDPE) and poly dimethyl siloxane (PDMS) rubber have been identified as a promising material for cable insulation. The influence of processing parameters on the physico-mechanical properties of these blends has been investigated and the same has been optimised for preparing these blends. In practice, it is not possible to carry out several experiments to identify the relationship between the intermediate processing parameters and physical properties. To address this issue, the relationship between the processing parameters and the mechanical properties of the optimised LLDPE and PDMS rubber blends have been mapped using a non-linear system identification technique namely, adaptive-network-based fuzzy inference system (ANFIS) without carrying further experiments, but with the available experimental data. In this model, the effect of the number of fuzzy rules on the model performance has been investigated to predict the properties and to correlate the above relationship. This paper reports the development of ANFIS-based model for predicting the LLDPE/PDMS rubber blends in reducing the experimental trials and its success over the ANN model.

Keywords:

• ANFIS
• NEURO-FUZZY
• POLYMER BLENDS
• PHASE MORPHOLOGY
• MECHANICAL PROPERTIES
• THERMOPLASTIC ELASTOMERS