Optimization and prediction of tribological performance in HNT/LLDPE composites for rotational molding using machine learning and Taguchi–TOPSIS

The current research focuses on predicting the tribological behavior of HNTs/LLDPE nanocomposites prepared via a rotational molding process through diverse techniques, including experimental design, multi-criteria decision-making, and machine learning classifier algorithms. The experimental design plan as per Taguchi’s L 16 orthogonal array has been created by varying the composition of HNT (1, 3, 5 and 7%), sliding velocity (1.5, 2, 2.5 and 3 m/s) and applied load (10 and 20 N). The evaluated specific wear rate (SWR) and coefficient of friction (COF) of the prepared samples are analyzed through signal-to-noise ratio method and ANOVA. The main effect plot and ranking from the response table highlighted that the composition of HNT is highly influential over both the output responses studied. ANOVA results indicate that the composition of HNT has 50% and 74.25% influence over specific wear rate and coefficient of friction. Through the combined approach of Entropy-CRITIC-TOPSIS techniques, the alternatives have been ranked and 55.22% of the contribution is shown by HNT composition and 26.06% of the contribution by sliding velocity over the combined objectives. The experimental outcomes have been analyzed as labelled data and the support vector machine classifier has rendered a superior performance of 93.8% of classification accuracy for both the experimental outcomes. The worn surface analysis through SEM revealed the presence of microcracks, wear debris and HNT particles. In applications requiring high durability and peak wear performance, the study suggests a greater HNT composition to lower the specific wear rate and coefficient of friction for the developed HNTs/LLDPE composite material.