In recent years, the development and use of polymeric nanocomposites in creating advanced materials has expanded exponentially. A substantial amount of research has been done in order to design polymeric nanocomposites in a safe and efficient manner. In the present study, the impact of processing parameters, such as, barrel temperature, and residence time on the mechanical and thermal properties of high density polyethylene (HDPE)-TiO₂ nanocomposites were investigated. Additionally, scanning electron microscopy and X-ray diffraction spectroscopy were used to analyze the dispersion, location, and phase morphology of TiO₂ on the HDPE matrix. Mechanical tests revealed that tensile strength of the fabricated HDPE-TiO₂ nanocomposites ranged between 22.53 and 26.30 MPa, while the Young’s modulus showed a consistent increase as the barrel temperature increased from 150 °C to 300 °C. Moreover, the thermal stability decreased as the barrel temperature increased.

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO₂ and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO₂ and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.