Informatics based design of prosthetic Ti alloys

Abstract

Informatics based approaches are employed to find a suitable composition of Ti alloy, with high strength, low elastic modulus, adequate biocompatibility and low cost. Artificial neural network, capable of prediction and diagnosis in non-linear and complex systems, is used to obtain the relationship of composition and processing parameters with elastic modulus and yield strength. As the objectives are conflicting, multiobjective optimisation using genetic algorithm is employed to optimally design titanium alloys suitable for prosthetic applications using the above models as objective functions for the mechanical properties. The Pareto solutions provide the desired alloy compositions where such properties may be achieved.

Imprecise knowledge based design and development of titanium alloys for prosthetic applications

“…single crystal elastic constants of Mg-Li alloys using density functional theory (DFT) [23], semiconductor spintronics [24], and Pb(Sc0.5Nb0.5)O3 perovskite alloy [25] etc. However, informatics-based design using computational intelligence techniques have been successfully used for designing Ti alloys [26][27] and other materials [28][29][30]. Informatics based materials design strategies try to extract the inherent correlation within data and utilize the information for designing novel materials [31].…”

Orthopaedic biomaterials: current status and future perspective

“…The effectiveness of a genetic algorithim based approach to design improved titanium based alloys for orthopaedic applications is explored in a further contribution. 7 Surface engineering of titanium alloys by hydroxyapatite (HA) coating is another recent innovation that has come to the fore in the past decade, represented here by studies on microplasma spraying 8 and the nanotribological properties of the resulting coatings. 9 One approach to enhance the bactericidal properties of biomaterials is to incorporate Ag/ZnO in a HA matrix.…”

Design optimization of effective modulus through variation in pore geometries of Stainless Steel 316L

“…An optimum combination of the design variable scan gives the required input parameters to achieve the desired output values. Genetic algorithm (GA) [19] is as an effective optimization tool to design materials, machines and machinery components with improved performance [20,21].In this work, GA is used to obtain the combination of parameters to achieve desired elastic modulus in the x and y direction. Use of FEA outputs as the objective functions for the optimization process is computationally expensive.…”