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• C and 60• 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.

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In this study, the morphology and spectroscopic properties of polypropylene (PP)/ polyethyleneterephthalate (PET) blends were investigated by PET addition to improve the mechanical properties of polypropylene. For the morphological characteristics of the PP + PET blends, SEM analysis for the spectroscopic properties and FT-IR analysis were performed. As the percentage of the mixture increases in the SEM images, there is a gap between the PET and the structure depending on the addition ratio. In the PP + 40% PET mixture, the transition from droplet form to fibril form appears clearly in the structure.