Design of the ultrahigh molecular weight polyethylene composites with multiple nanoparticles: An artificial intelligence approach

Abstract

This study proposes a suitable composite material for acetabular cup replacements in hip joint that involves ultrahigh molecular weight polyethylene, a clinically proven material, as the matrix. To design new ultrahigh molecular weight polyethylene composites with multiple reinforcements for the improvement in mechanical and tribological performance, artificial neural network and genetic algorithm, the two artificial intelligence techniques, are employed. Published reports on the use of ultrahigh molecular weight polyethylene reinforced with multi-walled carbon nanotube and graphene are used as database to develop two artificial neural network models for Young's modulus and tensile strength. The optimum solutions are obtained using genetic algorithm, where the artificial neural network models are used as the objective functions. Two different composites, derived from the optimum solutions, are made reinforcing both multi-walled carbon nanotube and graphene. Tensile and wear tests show significant enhancement in the properties. The structures of the composites are also characterized, and wear mechanisms are discussed.

Interfacial Engineering of Particulate & Surfactant Systems for Enhanced Performance in Industrial Applications

“…For example, Shams et al employed AI methods to estimate and optimize the silver nanoparticle concentration on the treated silk fabrics for antimicrobial properties (Shams Nateri et al, 2019). Vinoth and Datta used the artificial neural network and genetic algorithm to design a novel nanoparticle reinforced polymer composite for replacements in hip joints (Vinoth and Datta, 2020). Mahmoud et al implemented the artificial neural network to simulate and predict the removal efficiency of several organic contaminates from aqueous systems using iron nanoparticle sorbents (Mahmoud et al, 2018).…”

Current Trends and Applications of Machine Learning in Tribology—A Review

“…With R 2 values for training and testing above 0.8 as well as mean absolute errors not exceeding 4.1%, it was thus shown that sliding speed and load determined the wear losses more significantly than the particle types and fractions. Recently, Vinoth and Datta [53] also used 153 experimental data sets from literature to predict mechanical properties of UHMWPE composites with multi-walled carbon nanotubes (MWCNT) and graphene reinforcements in dependency of seven input variables comprising composite composition, particle size, and mechanical bulk properties. A feed forward ANN with scaled conjugate gradient back propagation, hyperbolic tangent transfer functions and 3 (for Young's modulus) or 5 (for the ultimate tensile strength) hidden layers were utilized, achieving correlation coefficients for the outputs of 0.93 and 0.97, respectively.…”

“…A few studies, however, manage to extract real insights and thus additional knowledge from a large and broad database. The comprehensive works in the field of composite materials from Kurt and Oduncuoglu [52], Vinoth and Datta [53], and Hasan et al [63,64] utilizing literature-extracted databases may be highlighted here and can serve as excellent examples. The current showstopper is still the availability of sufficient and comparable datasets as well as the handling of uncertainties regarding test conditions and deviations.…”

The Use of Artificial Intelligence in Tribology—A Perspective

“…In addition, various researchers have applied ML and AI approaches to predict and optimize the tribological behavior of different materials and operating conditions with manifold applications in mind [14][15][16][17][18][19][20]. For instance, Alambeigi et al [21] investigated the accuracy of predictive AI models by comparing them with experimental results obtained by testing the dry sliding contact of sintered steels [21].…”