Comparison of Mechanical Properties and Artificial Neural Networks Modeling of PP/PET Blends

Abstract

The aim of this study is to show the applicability of artificial neural networks, which are getting more applications with the advancement of technology, to determine the mechanical properties of polymeric materials. Mechanical properties of pure polypropylene, polyethylene terephthalate and their blends are determined in this study and the effect of temperature (room temperature, 40 degrees C and 60 degrees C) on mechanical properties is investigated. The method of artificial neural networks is used to make a prediction for mechanical properties. Mechanical properties of samples are measured using Lloyd 250N capacity tension and compression apparatus at crosshead speed of 10 mm/min, 25 mm/min, and 50 mm/min. For artificial neural networks modelling, the tensile experiment results, temperature, percent ratio, and crosshead speed are used as the input and output parameters. Three-layered multilayer perceptron, feed-forward neural network architecture is used and trained with the error back propagation. The results obtained from the output of the network are compared with the experiment results. The suitability of the method is found to be satisfactory.