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

REFERENCES

• Gibson , N. ; Stamm , H. The use of alloys in prosthetic devices . Business Briefing: Medical Device Manufacturing & Technology 2002 , 48 – 51 .
   Google Scholar
• Park , J.B. ; Bronzino , J.D. (Eds.) Biomaterials: Principles and Applications ; CRC Press : Boca Raton , FL , 2002 .
   Google Scholar
• Wise , D.L. (Ed .) Biomaterials and Bioengineering Handbook ; CRC Press : Boca Raton , FL , 2000 .
   Google Scholar
• Ratner , B.D. ; Hoffman , A.S. ; Schoen , F.J. ; Lemons , J.E. (Eds .) Biomaterials Science: An Introduction to Materials in Medicine, , 2nd Ed. ; Academic Press , 2004 .
   Google Scholar
• Hin , T.S. (Ed .) Engineering Materials for Biomedical Applications (Biomaterials Engineering and Processing) ; World Scientific Publishing Co. Pte. Ltd. : Singapore , 2004 .
   Google Scholar
• Niinomi , M. Recent research and development in titanium alloys for biomedical applications and healthcare goods . Science and Technology of Advanced Materials 2003 , 4 , 445 – 454 .
   Web of Science ®Google Scholar
• Bhargava , S. ; Dulikravich , G.S. ; Murty , G.S. ; Agarwal , A. ; Colaço , M.J. Stress corrosion cracking resistant aluminum alloys: Optimizing concentrations of alloying elements and tempering . Materials and Manufacturing Processes 2011 , 26 , 363 – 374 .
   Web of Science ®Google Scholar
• Rivera-Diaz-del-Castillo , P.E.J. ; Xu , W. Heat treatment and composition optimization of nanoprecipitation hardened alloys . Materials and Manufacturing Processes 2011 , 26 , 375 – 381 .
   Web of Science ®Google Scholar
• Dulikravich , G.S. ; Kumar , A. ; Egorov , I.N. Titanium Based Alloy Chemistry Optimization for Maximum Strength, Minimum Weight and Minimum Cost Using JMatPro and IOSO Software . In TMS Annual Meeting, Materials Informatics: Enabling Integration of Modeling and Experiments in Materials Science ; Rajan , K. , (Ed.); New Orleans , LA , March 9–13, 2008 .
   Google Scholar
• Deb , K. Multi-Objective Optimization Using Evolutionary Algorithms ; Wiley : Chichester , UK , 2001 .
   Google Scholar
• Zhang , Q. ; Mahfouf , M. A new Reduced Space Searching Algorithm (RSSA) and its application in optimal design of alloy steels. Proc. 2007 IEEE Cong. Evolutionary Computation, Singapore, 2007; pp. 1815–1822.
   Google Scholar
• Zadeh , L.A. Outline of a new approach to the analysis of complex systems and decision processes . IEEE Transactions on Systems, Man, and Cybernetics 1973 , 3 , 28 – 44 .
   Web of Science ®Google Scholar
• Niinomi , M. Fatigue performance and cyto-toxicity of low rigidity titanium alloy, Ti–29Nb–13Ta–4.6Zr . Biomaterials 2003 , 24 , 2673 – 2683 .
   PubMed Web of Science ®Google Scholar
• Kuroda , D. ; Niinomi , M. ; Morinaga , M. ; Kato , Y. ; Yashiro , T. Design and mechanical properties of new β type titanium alloys . Materials Science and Engineering A 1998 , 243 , 244 – 249 .
   Web of Science ®Google Scholar
• Davidson , J.A. ; Kovacs , P. Biocompatable, Low Modulus, Titanium Alloys for Medical Implants ; United States Patent, Patent Number 5169597, 1999.
   Google Scholar
• Schneidera , S. ; Schneidera , S.G. ; Marques da Silva , H. ; Neto , C.M. Study of the non-linear stress-strain behavior in Ti-Nb-Zr alloys . Materials Research 2005 , 8 , 435 – 438 .
   Google Scholar
• Dumbleton , J.H. ; Gustavson , L.J. ; Wang , K.K. High Strength, Low Modulus, Luctile Biocompatible Ti alloys. United States Patent, Patent Number 4857269, 1989.
   Google Scholar
• Donachie , M.J. , Jr. Titanium: A Technical Guide, , 2nd Ed. ; ASM International : Materials Park , OH , 2000 .
   Google Scholar
• He , G. ; Hagiwara , M. Ti alloy design strategy for biomedical application . Materials Science and Engineering C 2006 , 26 , 14 – 19 .
   Google Scholar
• Schneider , S.G. ; Nunes , C.A. ; Rogero , S.O. ; Higa , O.Z. ; Bressiani , J.C. Mechanical properties and cytotoxic evaluation of the Ti-3Nb-13Zr alloy . Biomecánica 2000 , 8 , 84 – 87 .
   Google Scholar
• Kosko , B. Neural Networks and Fuzzy Systems ; Prentice-Hall : Englewood Cliffs , NJ , 1992 .
   Google Scholar
• Mamdani , E.H. ; Assilian , S. An experiment in linguistic synthesis with a fuzzy logic controller . International Journal of Man-Machine Studies 1975 , 7 , 1 – 13 .
   Google Scholar
• Hong , T.P. ; Kuo , C.S. ; Chi , S.C. Trade-off between computation time and number of rules for fuzzy mining from quantitative data . International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2001 , 9 , 587 – 604 .
   Web of Science ®Google Scholar
• Ishibuchi , H. ; Yamamoto , T. ; Nakashima , T. Fuzzy data mining: Effect of fuzzy discretization. In Proceedings of the First IEEE International Conference on Data Mining, 2001; 241–248.
   Google Scholar
• Zhang , Q. ; Mahfouf , M. A nature-inspired multi-objective optimization strategy based on a new reduced space searching algorithm for the design of alloy steels . Engineering Applications of Artificial Intelligence 2010 , 23 , 660 – 675 .
   Web of Science ®Google Scholar
• Weiss , I. ; Semiatin , S.L. Thermomechanical processing of alpha titanium alloys—An overview . Materials Science and Engineering A 1999 , 263 , 243 – 256 .
   Web of Science ®Google Scholar
• Metalfirst.Com (www.metalfirst.com)
   Google Scholar
• Polmear , I.J. Light Alloys: From Traditional Alloys to Nanocrystals, , 4th Ed. ; Butterworth–Heinemann : London , UK , 2006 .
   Google Scholar
• Branke , J. ; Deb , K. ; Dierolf , H. ; Osswald , M. Finding knees in multi-objective optimization . In Parallel Problem Solving From Nature (PPSN-VIII) ; et al. . (Ed.); LNCS 3242. Springer : Heidelberg , 3242: 722–731 .
   Google Scholar