Polymer nanocomposites have found wide industrial applications, necessitating optimal mechanical and fracture properties evaluation, traditionally done through costly experimental methods. This study employs machine learning, particularly XGBoost, to predict properties like tensile and fracture properties swiftly, aiding material innovation across industries. The research investigates unvulcanized and vulcanized polypropylene (PP)/ethylene propylene diene monomer (EPDM) reinforced with clay and halloysite nanoparticles (HNT), analyzing fracture properties via essential work of fracture (EWF). Experimental design selects tests, and an XGBoost model predicts tensile strength and modulus, strain at break, EWF, and non‐EWF based on EPDM and nanoparticle percentages, composite and nanoparticle types. The model accurately predicts tensile strength and modulus but less so for strain at break, EWF, and non‐EWF. Mean Absolute Percentage Error values for training/test are 0.49/1.21, 1.05/1.55, 34.21/42.76, 3.02/14.35, and 2.89/3.78, with determination coefficients of 0.99/0.98, 0.99/0.97, 0.97/0.91, 0.97/0.79, and 0.92/0.73. Nanoparticles mainly affect outputs, with EPDM secondarily impactful, while composite and nanoparticle types exhibit similar significance. The best‐performing polymer nanocomposite is a dynamically vulcanized one containing 10 wt% EPDM and 3 wt% clay, achieving tensile strength of 25.070 MPa, tensile modulus of 261.170 MPa, EWF of 75.300 N/mm, and non‐EWF of 10.150 N/mm2.Highlights
The effects of ethylene propylene diene monomer (EPDM), clay and halloysite nanoparticles on the mechanics of polypropylene‐based nanocomposites.
Essential work of fracture (EWF) was used to study the fracture properties.
Machine learning was employed to predict all mechanical characteristics.
The vulcanization process improved all mechanical characteristics.
The best compound: vulcanized one containing 10 wt% EPDM and 3 wt% clay.