Abstract
A dental implant with three distinct layers, of titanium alloy at core, porous titanium alloy at the intermediate layer and titanium alloy hydroxyapatite composite at the outer layer, is designed to achieve low elastic modulus and adequate strength with bioactive surface. Artificial Neural Network (ANN) along with Rule of Mixture (ROM) is used to generate the objective functions for the Genetic Algorithm (GA) based multi-objective optimization for achieving the optimal designs, which are validated using Finite Element Analysis (FEA) simulations. The composition and processing parameters are correlated with the yield strength and elastic modulus of titanium alloy using ANN. The ANN models are generated to express the strength and effective modulus of the implant using ROM. To determine the optimal composition of titanium alloys, porous layers, and composite layers for a three-layer dental implant, multi-objective genetic algorithm is employed. The Pareto optimal solutions provide the guidelines for designing the implant. A few selected non-dominated solutions are used for studying the actual stress distribution at the bone-implant interface using FEA, and showed significant improvements compared to conventional implants.
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