Evaluation of the Utility of Using Classification Algorithms when Designing New Polymer Composites

Polymer composites are the materials that can be successfully used in the places where high mechanical strength and chemical resistance as well as low absorbability are required. These unique features of polymer composites are obtained mainly due to a suitably selected binder, i.e. a synthetic resin. At the same time, this component accounts for the high production costs of these materials. Partial substitution of the resin with glycolisates obtained using poly(ethylene terephthalate) waste (PET), helps reduce the price of polymeric mortars, while maintaining favourable physicomechanical properties. This modification method also has a beneficial effect on the environment, as it allows the utilisation of a very common waste, which is difficult to dispose of. The article concerns three types of resin mortars, i.e. epoxy, polyester and polyester with the addition of colloidal silica, modified with PET glycolisate. On the basis of the obtained data set and database knowledge mining techniques, such as discriminant analysis and decision trees, it was shown to what extent the type of resin and the presence of an added modifier differentiate the mortar properties. The results obtained with both methods were compared. It was confirmed that these techniques are effective both in the classification and prediction of the type (selection) of mortar in the process of designing new composites.