XGBoost machine learning assisted prediction of the mechanical and fracture properties of unvulcanized and dynamically vulcanized PP/EPDM reinforced with clay and halloysite nanoparticles

Author:

Rajaee Pouya1,Rabiee Amir Hossein2,Ashenai Ghasemi Faramarz1ORCID,Fasihi Mohammad3ORCID,Mahabadifar Mahdi1,Nedaei Shekarab Mahmoud1

Affiliation:

1. Department of Solids Design, Faculty of Mechanical Engineering Shahid Rajaee Teacher Training University Tehran Iran

2. School of Mechanical Engineering Arak University of Technology Arak Iran

3. School of Chemical, Petroleum and Gas Engineering Iran University of Science and Technology Tehran Iran

Abstract

AbstractPolymer 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.

Funder

Shahid Rajaee Teacher Training University

Publisher

Wiley

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