Technical Brief

A Genetic Algorithm Based Multi-Objective Shape Optimization Scheme for Cementless Femoral Implant

[+] Author and Article Information
Souptick Chanda, Dilip Kumar Pratihar

Department of Mechanical Engineering,
Indian Institute of Technology Kharagpur,
Kharagpur, West Bengal 721 302, India

Sanjay Gupta

Department of Mechanical Engineering,
Indian Institute of Technology Kharagpur,
Kharagpur, West Bengal 721 302, India
e-mail: sangupta@mech.iitkgp.ernet.in

1Corresponding author.

Manuscript received March 25, 2014; final manuscript received November 5, 2014; published online January 29, 2015. Assoc. Editor: David Corr.

J Biomech Eng 137(3), 034502 (Mar 01, 2015) (12 pages) Paper No: BIO-14-1136; doi: 10.1115/1.4029061 History: Received March 25, 2014; Revised November 05, 2014; Online January 29, 2015

The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

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Fig. 4

Finite element model of the implanted femur subjected to musculoskeletal loading conditions of normal walking and stair climbing. Finer mesh density was used at the implant-bone interface as indicated in the sectional view of the FE model. For magnitude of loading, see Table 1.

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Fig. 3

Probable shapes of the implant key sections: a comparison between the two parameterization schemes

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Fig. 2

Design parameters used in the study: (a) eighteen design variables characterizing four key sections of the implant and (b) initial implant model based on a collarless TriLock (DePuy) prosthesis

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Fig. 1

Parametric scheme for evolution of key sections of the implant: ellipse (p = 2) to superellipse (p > 2)

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Fig. 8

Three OSGs with four key transverse sections: (a) OSG-1, (b) OSG-2, and (c) OSG-3

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Fig. 10

A probability plot of the initial and optimal geometries (OSGs) based on local failure values (FL) obtained along implant-bone interface

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Fig. 9

The distribution of local failure values (FL) obtained along the posterior and anterior parts of implant-bone interface, respectively, of (a) initial model, (b) OSG-1, (c) OSG-2, and (d) OSG-3

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Fig. 5

A schematic representation of the optimization procedure

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Fig. 6

A scatter plot of the feasible solutions with the direction of optimization indicated by arrowheads

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Fig. 7

Pareto-optimal front of solutions: proximal resorbed BMF (%) versus global interface failure. The encircled markers indicate three dominant trade-off stem geometries which correspond to stem profiles OSG-1, OSG-2, and OSG-3, respectively.

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Fig. 11

A comparison of bone resorption in the proximal femur for different implants. The plots (a), (b), (c), and (d) correspond to immediate postoperative bone density distribution for OSG-1, OSG-2, OSG-3, and initial stem, respectively. The plots (e), (f), (g), and (h) correspond to bone density distribution after bone remodeling for OSG-1, OSG-2, OSG-3, and initial stem, respectively. Negative sign indicates bone resorption and the number indicates average reduction in bone density. Gray-scale variations represent bone density ranges in g cm−3.




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