Optimal Design of Titanium Alloys for Prosthetic Applications Using a Multiobjective Evolutionary Algorithm

Multiobjective optimization using a Reduced Space Searching Algorithm (RSSA) is employed to optimally design titanium alloys suitable for prosthetic applications, i.e., with high strength, low elastic modulus, adequate biocompatibility, and low costs. The objectives in question are conflicting in nature, and thus multiobjective optimization is the ideal candidate for approaching this problem. The latter was formulated in such a way that it was necessary to develop three separate objective functions for strength, elastic modulus, and economic costs. The biocompatibility issue was introduced as a constraint in the optimization process. To develop the objective functions for yield strength and elastic modulus, a two-layered fuzzy inference system is used. To take into account economical factors, a weighted sum-based model of the elemental constituent is developed, including the costs of the alloying additions. The compositions of the alloy found from the Pareto solutions show that the above objectives can be fulfilled in the case of β Ti-alloys only.

[1]  Mitsuo Niinomi,et al.  Fatigue performance and cyto-toxicity of low rigidity titanium alloy, Ti-29Nb-13Ta-4.6Zr. , 2003, Biomaterials.

[2]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[3]  Donald L. Wise,et al.  Biomaterials and Bioengineering Handbook , 2000 .

[4]  I. Polmear,et al.  Light Alloys: From Traditional Alloys to Nanocrystals , 2006 .

[5]  Wei Xu,et al.  Heat Treatment and Composition Optimization of Nanoprecipitation Hardened Alloys , 2011 .

[6]  M. Niinomi,et al.  Design and mechanical properties of new β type titanium alloys for implant materials , 1998 .

[7]  M. Donachie.  Titanium: A Technical Guide , 1988 .

[8]  Mahdi Mahfouf,et al.  A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels , 2010, Eng. Appl. Artif. Intell..

[9]  Tzung-Pei Hong,et al.  Trade-off Between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Kalyanmoy Deb,et al.  Finding Knees in Multi-objective Optimization , 2004, PPSN.

[11]  Hisao Ishibuchi,et al.  Fuzzy data mining: effect of fuzzy discretization , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[12]  S. Semiatin,et al.  Thermomechanical processing of alpha titanium alloys : an overview , 1999 .

[13]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[14]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[16]  Mahdi Mahfouf,et al.  A new Reduced Space Searching Algorithm (RSSA) and its application in optimal design of alloy steels , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  Buddy D. Ratner,et al.  Biomaterials Science: An Introduction to Materials in Medicine , 1996 .

[18]  Guo He,et al.  Ti alloy design strategy for biomedical applications , 2006 .

[19]  J. Bronzino,et al.  Biomaterials : Principles and Applications , 2002 .

[20]  C. M. Neto,et al.  Study of the non-linear stress-strain behavior in Ti-Nb-Zr alloys , 2005 .

[21]  Mitsuo Niinomi,et al.  Recent research and development in titanium alloys for biomedical applications and healthcare goods , 2003 .