Prediction of Halogen-Free and Flame Retardant (HFFR) Polymeric Composite Sheathed Cable Elongation Test Results Using Machine Learning Methods

Recently, there has been an increasing interest in the use of artificial intelligence techniques in different fields. In this work is aimed to use different machine learning algorithms (MLA) to predict the elongation at break from the mechanical properties of cable sheath materials in halogen-free flame retardant (HFFR) cables. In order to be used in the developed prediction models, tensile test was applied to the samples and the percent elongation values were determined. Obtained experimental results were used in different artificial intelligence prediction models. Absolute percentage errors of support vector machine (SVM) and artificial neural network (ANN) methods were obtained at a quite acceptable level with a limited number of data obtained from HFFR cable samples. The estimations obtained by these methods were compared with the data of the estimations obtained by performing regression analysis with the MS Excel program. According to the statistical results, with the use of SVM and ANN in this area, the successful prediction rate was 87.5%, and the average success rate for the predictions made was 92%. The use of MLA in this area will largely end the uncertainty in the trial and error production and reduce the rate of unsuccessful production.