Advanced machine learning and experimental studies of polypropylene based polyesters tribological composite systems for sustainable recycling automation and digitalization

Abstract

Digitalization and automation are emerging solutions to the complex problems of recycling. In this research work, the experimental and Python based Archard deep learning wear rate models are introduced regarding recycling automation and composite tribological systems optimization. The optimum polyester fibers (PESF) of length of 3–3.5 mm were used for fabrication of polypropylene (PP)-PESF composite systems. The deformation, high texture, asperities, and micro-cracks were observed during scanning electron microscope and machine-learning studies. The lowest experimental value of abrasive wear of 3.0 × 10⁻⁶ mm³/Nm was observed for PP. Comparatively, higher experimental values of abrasive wear of the PP-PESF composites are found in the range of 4.35 × 10⁻⁶ to 4.7 × 10⁻⁶ mm³/Nm due to presence micro-defects on the surface of composites. The experimental values of Coefficient of friction (COF) of PP and PP-PESF are found in the range of 0.70–0.8 and 1.1–1.3, respectively. The experimental values of abrasive wear and COF are found compatible with literature. Similarly, the simulated values of abrasive wear of PP and PP-PESF composites are predicted in the range of 4.8 × 10⁻⁷ to 3.75 × 10⁻⁷ mm³/Nm, respectively. The predicted values of PP and PP-PESF composite show better resistance towards abrasive wear. The proposed experimental and simulated (in terms of Python coding, machine learning, image processing, artificial intelligence, and deep learning studies) research work can be introduced industrially for automation as well as digitalization of grinding of PES waste, processing, tribological testing, and SEM characterization evaluations.